**1. Introduction**

The advancement of electric vehicles is driven by the ambition to reduce emissions to increase the consumption of fuels [1]. In this situation of India and China, the shortage of energy is anticipated to happen rapidly because of a reduction in the future availability of fossil fuels and a 76% hike in necessity in the period from 2020 to 2045 [2]. Carbon emissions and waste are decreased by employing renewable energy [3]. Due to this, there is an increase in demand for clean, pollution-free renewable energy that emits only 30 carbon dioxides [4]. Industry and researchers have utilized advanced PV modules for many purposes due to consecutive reductions in the price of PV panels and power electronics components [5]. To maximize a PV array's capacity, the MPPT approach with a DC-DC converter topology is commonly utilized [6]. No carbon emissions are produced. Industry and researchers have utilized the advanced solar PV array for many applications because

**Citation:** Radhakrishnan, R.K.G.; Marimuthu, U.; Balachandran, P.K.; Shukry, A.M.M.; Senjyu, T. An Intensified Marine Predator Algorithm (MPA) for Designing a Solar-Powered BLDC Motor Used in EV Systems. *Sustainability* **2022**, *14*, 14120. https://doi.org/10.3390/ su142114120

Academic Editors: Nien-Che Yang and Shuhua Fang

Received: 11 September 2022 Accepted: 21 October 2022 Published: 29 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

of continuous reductions of price in power electronics components and PV panels [7]. The MPPT approach with DC-DC converters typically maximizes a PV array's capacity [8]. Various MPPT control techniques have been proposed, with fractional open/short-circuit control methods, incremental conductivity (INC), and perturbation and observation (P&O) being the most often used conventional techniques. These techniques result in a high turnout in a steady-state activity [9]. These algorithms were verified to be not effective as when the weather is bad, conversion ratios are slow, and bigger variances prevent them from obtaining an overall maximum power point (MPP) in settings with partial shading conditions. To deal with these problems, MPPT with a bioinspired optimization algorithm has been proposed.

The artificial immune system (AIS) and the metaheuristic genetic algorithm (GA) were applied to overcome such nonlinear uncertain conditions because of the appropriate particular sensor and the complicated circuitry [10]. However, immune cells have a huge population structure and adaptive machinery, which results in a poor conversion rate and a lengthy conversion time for AIS and GA algorithms [11]. Crossover procedures are used in conjunction with computational convergence time to enhance the mutation. Many MPPT techniques with bioinspired optimization are implemented to challenge such difficulty [12]. FSA is a fish life-inspired methodology designed to reduce grade point average assessment (GMPP) oscillations. Numerous control settings are needed for PSO's random accelerating value choosing, and it may be a significant drawback. The bioinspired optimization techniques presently have more tracking efficiency, a high convergence rate, and low transients [13]. Gray wolf (GW), ant colony (AC), glowworm optimization algorithm, and fish swarm algorithm (FSA) are a few examples. However, due to less bee availability and the weather being unpredictable, the poor conversion rate in ABC approaches [14,15].

Due to a shortfall of contingency and a heavy nest population, the cuckoo search algorithm is a more productive way for nonlinear-based issues, although its rate of melting is moderate. Due to this, several researchers have implemented this bioinspired approach based on photovoltaic system investigation. Considering the difficulty present in MPPT techniques, this paper proposed a novel MPPT control technique for MPA. It does not need hardware data from PV, as it can exactly and rapidly search to find the GMPP. This work object is to enhance the overall performance of PV-powered electric vehicles. In this technology, the BLDC motor is used in PV-powered e-vehicles. The MPA technique has been implemented to increase the complete performance of the system. The MPA technique features a faster convergence rate and a better method to locate GMPP. An observation of MPPT output has been illustrated to determine the effectiveness of the suggested approach in this framework.

For maximum power tracking from solar PV, making use of combined MPPT, different techniques were presented by scientists. In the following segment, the different MPPT approach for EVs driven by BLDC is surveyed, and it was designed for maximum power tracking.

The complete paper's structure is provided below: The literature review is presented in Section 2. the proposed work is explained in Section 3. Sections 4 and 5 give the control techniques which were used in the proposed work, followed by its results in Section 6 and conclusion in Section 7.


**1. Introduction**

The development of technologies related to genetic improvement, such as transgenesis and more recently, genome editing, have changed the way humans have grown food for thousands of years. Today, the most promising tool to DNA manipulation is CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats), a gene-editing technology that has been adapted from the bacterial immune system against viral infections [1]. Currently, a target site in the host genome can be reached by an endonuclease enzyme (i.e., Cas9) led by a guide RNA molecule (gRNA) that contains the target-specific

sequence. This protein complex forms the CRISPR-Cas9 ribonucleoproteins (RNPs) gene-editing system. Cas9 introduces a site-specific double-stranded DNA break (DSB) followed by the natural cell repair of disrupted genome integrity by error-prone non-homologous end-joining (NHEJ) or homology-directed repair (HDR) [2]. Therefore, this tool allows the in vivo modification of the DNA at the gene sequence of interest, with unprecedented speed, and has made it a milestone in manipulating and producing living modified organisms.

Although much is already known about the principles of CRISPR-Cas9 genome editing, the likelihood of different outcomes in terms of resolution, efficiency, accuracy, and DNA modification structure has shown to be species-dependent. Various factors, including target site choice, gRNA design, the properties of the endonuclease, the type of DSB introduced, whether or not the DSB is unique, the quantity of the endonuclease and gRNA, and the intrinsic differences in DNA repair pathways in different species, tissues, and cells will result in differences in the mutation signatures generated in plant species [3].

In the case of maize, most researchers acknowledge that *Zea mays* L. is an ancient amphidiploid species with a duplicated chromosome number of *n* = 10 [4]. Multiple independent domestication events have also contributed to the genetic variability encountered in modern maize [4]. In addition, the genetic variants and the underlying mechanisms influencing variance heterogeneity in maize have so far hidden additive genetic effects and epistatic interaction effects in elite varieties [5]. Therefore, CRISPR can be a useful tool to access genomic regions in the maize genome, which are difficult to achieve by conventional breeding.

While CRISPR technology has already been tested on commercial crops to increase yield, drought tolerance and growth under limited nutrient conditions, improve nutritional properties and develop resistance to plant pathogens [6]; breeding and research of major monocotyledon species, more specifically maize, are still at its infancy. Maize has shown to be an exemption in the plant portfolio for the in silico analysis of potential Cas9 target sites as only 29.5% of annotated transcripts matched a specific gRNA [7]. Among eight analyzed plant species, maize had the largest genome, the highest GC content, and the greatest number of annotated transcripts. Thus, reflecting the abundance of highly repetitive DNA and dispersed repeats, which may be challenging to develop unique target sites for the majority of genes in maize [7].

Despite such challenges, CRISPR technology opens up the possibility for genome changes without foreign introgression of DNA vectors. CRISPR-Cas9 technology can be used as RNP complexes without the introgression and expression of a transgenic cassette in the host genome [8]. Such an approach would avoid a number of generations of backcrossing, expression vectors, and other invasive methods of cell penetration (e.g., biolistics) that can lead to gene disruption, including large deletions, partial trisomy, genome shattering events, and plant mosaicism [9]. Overall, these side effects can mask or interfere in the target gene functional analysis, and further additional biosafety concerns prior to commercial release.

CRISPR RNPs can be delivered directly to plant cells without the cell wall. Therefore, prior to transfection, the cell wall must be removed through enzymatic digestion reactions. Protoplast cells are viable in vivo biological material for DNA-free CRISPR delivery in plants. In addition, protoplasts are viable after transfection, which allows further tissue cultivation and propagation.

Delivery of preassembled Cas9 protein-gRNA RNPs or plant DNA-free genome-editing techniques are not exempt from off-target effects but represent an approach in which the effects of Cas9 could be isolated from other more invasive techniques [8,10,11]. This approach was first demonstrated in *Arabidopsis thaliana*, tobacco, lettuce, and rice protoplasts, including the regeneration of gene-edited lettuce [12]. After that, a few successful attempts were also accomplished on grapevine and apple [13], *Petunia* × *hybrida* [14], potato [15], and on soybeans and tobacco using CRISPR-Cpf1 (CRISPR from *Prevotella* and *Francisella*), recently named Cas12a (for review, please read Metje-Sprink et al. [8]). Maize and wheat plants with targeted mutations have also been successfully obtained by delivering gold particles coated with the RNPs into embryo cells (biolistics), followed by post-bombardment culture

and plant regeneration [16]. However, the frequency of obtaining genome-edited plants was relatively low, since only 0.3–0.9% of regenerated maize plants possessed biallelic mutations [16].

The few studies published on maize genome editing rely mostly on the stable transformation [17–20]. In the manuscript, we delivered Cas9-gRNA RNP into maize leaf protoplasts via polyethylene glycol (PEG)-calcium mediated transfection, and indicated that In/Del mutations occurred with relatively high e fficiency of 0.85–5.85% among the PEG-calcium-treated protoplast. We targeted the *inositol phosphate kinase* gene (IPK) involved in the phytic acid biosynthetic pathway. To develop a standard protocol for di fferent maize varieties, we designed gRNAs and primers complementary to coding regions in exon three that are conserved in the species, in order to evaluate the e fficiency and spectrum of DNA changes generated by CRISPR-Cas9 technology in maize, and also add relevant information to the safety of gene-edited organisms. This e fficient and relatively easy assay method for the selection of gRNA suitable for editing of genes of interest will be highly useful for genome editing in maize, since the genome size and GC-content are large and high in the maize genome, respectively.


**6.1 Case 1 C56 ADR C67 ACDF**

51 year old female who presents with cervical pains which she describes as 80% neck pain and 20% arm/shoulder pain, which is 100% left-sided in a C6 and C7 distribution. MRI of the cervical spine demonstrated a C5-C6 3 mm disc herniation with facet arthropathy and severe bilateral foraminal stenosis (**Figure 7a,b**). At C6-C7 a 2 mm left paracentral disc protrusion was noted with severe bilateral foraminal stenosis (**Figure 7c**). For her pain, the patient had tried a prolonged course of conservative management in the form of physical therapy, heating pads, and ice packs. She had tried medications in the form of NSAID's, muscle relaxants and narcotics. She had consulted with pain management and undergone injection procedures in the form of transforaminal epidural injections at C56 and later

### **Figure 7.**

*(a) Sagittal MRI of the cervical spine demonstrated a C5-C6 and C67 disc herniations. (b) Axial MRI of the cervical spine demonstrated a C5-C6 bilobed herniation with severe bilateral foraminal stenosis. (c) At C6-C7 a 2 mm disc protrusion was noted with severe bilateral foraminal stenosis. (d) AP Xray artificial disc replacement at C56 and anterior cervical discectomy and fusion at C67. (e) Lateral Xray artificial disc replacement at C56 and anterior cervical discectomy and fusion at C67.*

at C67, which each provided one hundred percent pain relief and lasted for one month. Patient underwent an uncomplicated Artificial Disc Replacement at C56 and Anterior Cervical Discectomy and Fusion at C67, and has since noted complete resolution of her symptoms (**Figure 7d, e**).

## **6.2 Case 2 C45 ACDF C56 ADR C67 ACDF**

48 year-old female who presented with 95% neck pain and 5% shoulder pain, which is 50% right-sided and 50% left, sided in a C5, C6, and C7 distribution. MRI of the cervical spine demonstrated at the C4-C5 level moderate central spinal

### **Figure 8.**

*(a, b) Sagittal MRI of the cervical spine demonstrated herniations at the C4-C5, C56, C67. (c) Axial MRI of the cervical spine demonstrated C45 large disc herniation with neuroforaminal stenosis. (d) Axial MRI of the cervical spine demonstrated 56 eccentric disc herniations with left sided neuroforaminal stenosis. (e) Axial MRI of the cervical spine demonstrated C67 eccentric disc herniation with right sided neuroforaminal stenosis. (f) AP Xray artificial disc replacement at C56 and anterior cervical discectomy and fusion at C45, and C67. (g) LATERAL Xray artificial disc replacement at C56 and anterior cervical discectomy and fusion at C45, and C67.*

### *The Cervical Hybrid Arthroplasty DOI: http://dx.doi.org/10.5772/intechopen.95950*

canal stenosis, a 4 mm disc protrusion with moderate-to-severe neural foraminal, narrowing bilaterally and impingement on the exiting nerve roots bilaterally **Figure 8a-c**. At the C5-C6, level, there was a 4 mm left paracentral disc protrusion with severe neural foraminal narrowing on the left and moderate foraminal narrowing on the right. There is impingement on the exiting nerve roots bilaterally greater on the left than the right (**Figure 8d**). At the C6-C7 level, there was a 5 mm right paracentral disc protrusion with severe neural foraminal narrowing on the right with impingement on the exiting nerve roots on the right (**Figure 8e**). There is moderate neural foraminal narrowing on the left and moderate central spinal canal stenosis. For her pain, the patient had tried a prolonged course of conservative management in the form of physical therapy, chiropractic treatment, heating pads, and ice packs. She had tried medications in the form of NSAID's, muscle relaxants and narcotics. She had consulted with pain management and undergone three injection procedures in the form of transforaminal epidural injections at C45, C56 and later at C67, each of which provided seventy percent pain relief and lasted for one to three months. Patient underwent an uncomplicated Artificial Disc Replacement at C56 and Anterior Cervical Discectomy and Fusion at C45 and C67, and has since noted resolution of her symptoms (**Figure 8f**, **g**).


**9. Conclusion**

In wound defects, ADSCs presented a great ability in migration and were recruited rapidly into wounded sites where the process of cell differentiation toward various skin cell components occurred. However, ADSCs participate more likely in all the phases of wound healing through autocrine and paracrine pathways [6]. Otherwise, during aging, senescent cells increase and the paracrine senescent secretome of ADSCs can trigger and reinforce senescence within their microenvironment [124]. This paracrine effect can be transmitted by ligands of TGF-β by mediating changes in the transcriptional program through SMAD family members [146].

Another surprising capacity of these cells is that secretome derived from younger cells is more suitable to increase proliferation than that derived from older cell [18], suggesting that younger cells have the potential to secrete a youth growth factor identified as GDF11, able to quantitatively increase cell proliferation at the younger stage. Targeted cells are the other crucial parameters leading to this increase; younger cells presented less senescence characteristics including DNA damage and ROS accumulation, thus, inducing cell rejuvenation. This process might be used to directly induce secretome of these cells toward tissue regeneration or rejuvenation.

We cannot exclude that MSCs and ADSCs secreted other cytokines than GDF11 and TGF-β, such as PDGF, IL-1, bone morphogenic protein (BMP)6, BMP9, and exerted autocrine and paracrine effects on DF and keratinocytes, promoting cell differentiation, proliferation, and migration. Nevertheless, the antiaging paracrine effect seemed to be induced, perhaps not exclusively but at least to

**55**

*Adipose-Derived Stem Cells (ADSCs) and Growth Differentiation Factor 11 (GDF11)…*

a significant degree, by a combinatorial effect of both GDF11 and TGF-β. It is probable that both signals vary with age and that the strength of each of them is reciprocal to the sites of secreted signals and to the length of the exposure to the signal. Based on these considerations, further investigations on TGF-β and GDF11 molecular mechanisms' implication on skin rejuvenation are needed to increase our knowledge and draw conclusions on the regulation of aging process. Exciting therapeutic approaches might arise from the implication of GDF11 as an antiaging mechanism to increase the lifespan and the long-lasting functionality of different

The authors wish to thank the University of Mohammed VI Polytechnic for

*DOI: http://dx.doi.org/10.5772/intechopen.91233*

organs.

**Acknowledgements**

funding this work.

**Conflict of interest**

**List of abbreviations**

The authors declare no conflict of interest.

ADSCs adipose-derived stem cells GDF11 growth differentiation factor TGF-β transforming growth factor

VEGF vascular endothelial growth factor PDGF platelets derived growth factor

MCP-1 monocyte chemoattractant protein-1

ECM extracellular matrix DF dermal fibroblasts ROS reactive oxygen species MSCs mesenchymal stem cells MMP1, 2, 9 matrix metalloproteinase1, 2, 9 IL-1, −6, −8, −10 interleukin-1, −6, −8, −10 TNF-α tumor necrosis factor-α

α-SMA α-smooth muscle actin

*Adipose-Derived Stem Cells (ADSCs) and Growth Differentiation Factor 11 (GDF11)… DOI: http://dx.doi.org/10.5772/intechopen.91233*

a significant degree, by a combinatorial effect of both GDF11 and TGF-β. It is probable that both signals vary with age and that the strength of each of them is reciprocal to the sites of secreted signals and to the length of the exposure to the signal. Based on these considerations, further investigations on TGF-β and GDF11 molecular mechanisms' implication on skin rejuvenation are needed to increase our knowledge and draw conclusions on the regulation of aging process. Exciting therapeutic approaches might arise from the implication of GDF11 as an antiaging mechanism to increase the lifespan and the long-lasting functionality of different organs.


**5. Conclusions**

Herbal saponins, tea, were studied to find alternatives to synthetic surfactants that are commonly used and were make comparison with marketed available surfactant (Surf Excel). The outcomes demonstrated that saponins are naturally acidic and decomposable. Natural materials are considered to be biodegradable as plant extracts. The tea, which was probably examined by the first time, shows good effectiveness, in addition to high foaming capacity, decent cleaning capacity and ultra-soft. Although lots of works account on isolation, characterization, etc. Tea has good emulsifiers and can find some industrial uses. We also quantified the cleaning capacity of surfactant solutions. Therefore, our studies can offer a simple and inexpensive method to measure the general cleaning method for the evaluation of detergents. We conclude that tea comes with good surface-active properties. These studies can offer useful information for the food industry as well as the cosmetics industry reason was that these plant-based materials were biodegradable organic surfactant. We propose new biodegradable and renewable alternative from plant-based material which act as a surfactant.


*4.1.4. Scenario 4*

**4.2. Process PROC2**

**Figure 6.** Mean convergence in PROC1, Scenario 2 for (a) PSO with P = 20, (b) DE with P = 20 and (c) DE with P = 30.

#### *4.1.3. Scenario 3*

In this Scenario, all the weights constrains were relaxed, but the causalities were kept, i.e., the value of the weights were fixed in the interval [0, 1] or in the interval [−1, 0), according to the causality determined by the experts. Table 5 presents the obtained results. As can be seen, P = 10 was enough for achieving 100% of convergence in GA and PSO. With P = 10, DE was not able to find the proper solution in 36 trials, resulting in a probability of success equal to 0.9964, but when P = 20, DE obtained 100% of success. Figure 7 presents the mean convergence of the concepts in 104 independent experiments. In this scenario, DE presented the fastest average convergence.

**Figure 7.** Mean convergence for Scenario 3 in PROC1 considering (a) PSO with P = 10, (b) GA with P = 10, (c) DE with P = 10, and (d) DE with P = 20.

**Table 5.** GA, PSO and DE simulation results for Scenario 3, PROC1.

#### *4.1.4. Scenario 4*

14 Search Algorithms

0.55 0.6 0.65 0.7 0.75 0.8 0.85

0.55 0.6 0.65 0.7 0.75 0.8 0.85

Concept Values

*4.1.3. Scenario 3*

Concept Values

0 10 20 30 40 50 0.5

(a)

Number of Iterations

the fastest average convergence.

0 10 20 30 40 50 0.5

(a)

P = 10, and (d) DE with P = 20.

Number of Iterations

C1 C2 C3 C4 C5

> C1 C2 C3 C4 C5

0.55 0.6 0.65 0.7 0.75 0.8 0.85

0.55 0.6 0.65 0.7 0.75 0.8 0.85

Concept Values

Concept Values

0.55 0.6 0.65 0.7 0.75 0.8 0.85

Concept Values

0 10 20 30 40 50 0.5

(b)

In this Scenario, all the weights constrains were relaxed, but the causalities were kept, i.e., the value of the weights were fixed in the interval [0, 1] or in the interval [−1, 0), according to the causality determined by the experts. Table 5 presents the obtained results. As can be seen, P = 10 was enough for achieving 100% of convergence in GA and PSO. With P = 10, DE was not able to find the proper solution in 36 trials, resulting in a probability of success equal to 0.9964, but when P = 20, DE obtained 100% of success. Figure 7 presents the mean convergence of the concepts in 104 independent experiments. In this scenario, DE presented

0 10 20 30 40 50 0.5

(b)

0 10 20 30 40 50 0.5

(d)

**Figure 7.** Mean convergence for Scenario 3 in PROC1 considering (a) PSO with P = 10, (b) GA with P = 10, (c) DE with

Number of Iterations

Number of Generations

**Figure 6.** Mean convergence in PROC1, Scenario 2 for (a) PSO with P = 20, (b) DE with P = 20 and (c) DE with P = 30.

Number of Iterations

C1 C2 C3 C4 C5

> C1 C2 C3 C4 C5

C1 C2 C3 C4 C5 0.55 0.6 0.65 0.7 0.75 0.8 0.85

Concept Values

0.55 0.6 0.65 0.7 0.75 0.8 0.85

Concept Values

0 10 20 30 40 50 0.5

0 10 20 30 40 50 0.5

(c)

Number of Iterations

(c)

Number of Iterations

C1 C2 C3 C4 C5

C1 C2 C3 C4 C5

> In this situation, the causality and the strength of the causality were totally relaxed. PSO and GA algorithms were able to determine proper weights for the connection matrix with P = 10, and DE did not have success in only 4 experiments, resulting in a probability of success equal to 0.9996. When P = 20, DE did not presented convergence errors. Table 6 presents the obtained results, while Figure 8 presents the mean convergence of the concepts in 104 independent experiments. As in Scenario 3, DE presented the fastest average convergence, while GA and PSO presented similar average convergence speed.

#### **4.2. Process PROC2**

For this process the value of the weights were fixed in the interval [0, 1] or in the interval [−1, 0), according to the causality determined by the experts. The adopted input simulation parameters for PSO, GA and DE algorithms were the same considered in PROC1, except for the population size in PSO that was assumed P = 10 and P = 20.

Table 7 summarizes the simulation results, while Figures 9 and 10 present the mean convergence of the concepts value considering 104 independent experiments. As can be seen, P = 10 was enough for achieving convergence rate of 100% in GA. With P = 10, PSO presented 30 failures and probability of success equal to 0.9970, while DE presented 897 failures and probability of success equal to 0.9103. However, with P = 20, both PSO and DE were able to achieve 100% of success. For this process, DE has a faster average convergence than PSO and GA, being PSO the second fastest.

**PSO with** P = 10 *A*max 0.7000 0.9199 0.8206 0.8404 0.8206 *A*min 0.6800 0.2129 0.4339 0.3952 0.7800 *A*average 0.6901 0.6908 0.6767 0.6777 0.8101

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**GA with** P = 10 *A*max 0.7000 0.9192 0.8206 0.8404 0.8206 *A*min 0.6800 0.2130 0.4339 0.3952 0.7800 *A*average 0.6905 0.6154 0.6371 0.6306 0.8030

**DE with** P = 10 *A*max 0.7166 0.9199 0.8206 0.8404 0.8229 *A*min 0.6800 0.2112 0.4285 0.3948 0.7628 *A*average 0.6901 0.6087 0.6358 0.6232 0.8084

**DE with** P = 20 *A*max 0.7000 0.9199 0.8206 0.8404 0.8206 *A*min 0.6800 0.2129 0.4338 0.3952 0.7800 *A*average 0.6902 0.6061 0.6311 0.6227 0.8102

There are in the literature, several models based on fuzzy cognitive maps developed and adapted to a large range of applications. The use of learning algorithms may be necessary for obtaining proper values for the weight matrix and reducing the dependence on the experts'

Within this context, this Chapter presented a comparison of three heuristic search approaches, PSO, GA and DE, applied to FCM weight optimization in two processes control. In all the considered cases, GA presented a performance in terms of probability of success better or equal to the other two schemes, being PSO the second best technique in terms of

Specially in scenario 2 of PROC 1, when there are several weight constraints, GA achieved 100% of success in 104 independent experiments with a population of 10 chromosomes. PSO needed P = 20 particles in the population in order to reach 100% of success. DE was not

Number of Failures: 0 Probability of Success: 1.0

Number of Failures: 0 Probability of Success: 1.0

Number of Failures: 4

Number of Failures: 0 Probability of Success: 1.0

**Table 6.** GA, PSO and DE simulation results for Scenario 4, PROC1.

probability of success in the two considered processes.

able to achieve 100% of success even for P = 30.

**5. Conclusions**

knowledge.

Probability of Success: 0.9996

**Figure 8.** Mean convergence for Scenario 4 in PROC1 considering (a) PSO with P = 10, (b) GA with P = 10, (c) DE with P = 10, and (d) DE with P = 20.

**Figure 9.** Mean convergence for (a) PSO, (b) GA and (c) DE in PROC2 with P = 10.

**Figure 10.** Mean convergence for (a) PSO and (b) DE in PROC2 with P = 20.

**Table 6.** GA, PSO and DE simulation results for Scenario 4, PROC1.

## **5. Conclusions**

16 Search Algorithms

0.55 0.6 0.65 0.7 0.75 0.8 0.85

0.5 0.55 0.6 0.65 0.7 0.75 0.8

Concept Values

Concept Values

0 10 20 30 40 50 0.5

(a)

P = 10, and (d) DE with P = 20.

0 10 20 30 40 50 0.45

(a)

0.5 0.55 0.6 0.65 0.7 0.75 0.8

Concept Values

Number of Iterations

Number of Iterations

C1 C2 C3 C4 C5 0.55 0.6 0.65 0.7 0.75 0.8 0.85

0.55 0.6 0.65 0.7 0.75 0.8 0.85

> 0.5 0.55 0.6 0.65 0.7 0.75 0.8

Concept Values

**Figure 9.** Mean convergence for (a) PSO, (b) GA and (c) DE in PROC2 with P = 10.

0 10 20 30 40 50 0.45

(a)

Number of Iterations

**Figure 10.** Mean convergence for (a) PSO and (b) DE in PROC2 with P = 20.

Concept Values

C1 C2 C3 C4 C5 C6 C7 C8 Concept Values

0 10 20 30 40 50 0.5

0 10 20 30 40 50 0.5

(d)

**Figure 8.** Mean convergence for Scenario 4 in PROC1 considering (a) PSO with P = 10, (b) GA with P = 10, (c) DE with

0 10 20 30 40 50 0.45

(b)

C1 C2 C3 C4 C5 C6 C7 C8

Number of Generations

0.5 0.55 0.6 0.65 0.7 0.75 0.8

Concept Values

Number of Iterations

(b)

Number of Generations

C1 C2 C3 C4 C5

C1 C2 C3 C4 C5

C1 C2 C3 C4 C5 C6 C7 C8

0.5 0.55 0.6 0.65 0.7 0.75 0.8

0 10 20 30 40 50 0.45

(b)

Number of Iterations

Concept Values

0.55 0.6 0.65 0.7 0.75 0.8 0.85

Concept Values

0 10 20 30 40 50 0.5

0 10 20 30 40 50 0.45

(c)

Number of Iterations

C1 C2 C3 C4 C5 C6 C7 C8

(c)

Number of Iterations

C1 C2 C3 C4 C5

> C1 C2 C3 C4 C5 C6 C7 C8

> > There are in the literature, several models based on fuzzy cognitive maps developed and adapted to a large range of applications. The use of learning algorithms may be necessary for obtaining proper values for the weight matrix and reducing the dependence on the experts' knowledge.

> > Within this context, this Chapter presented a comparison of three heuristic search approaches, PSO, GA and DE, applied to FCM weight optimization in two processes control. In all the considered cases, GA presented a performance in terms of probability of success better or equal to the other two schemes, being PSO the second best technique in terms of probability of success in the two considered processes.

> > Specially in scenario 2 of PROC 1, when there are several weight constraints, GA achieved 100% of success in 104 independent experiments with a population of 10 chromosomes. PSO needed P = 20 particles in the population in order to reach 100% of success. DE was not able to achieve 100% of success even for P = 30.

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**Table 7.** GA, PSO and DE simulation results for PROC2.

In most scenarios, DE has presented a faster convergence in comparison to PSO and GA, but the population size needed in DE was higher than the other two methods.

## **Author details**

Bruno Augusto Angélico1, Márcio Mendonça1, Lúcia Valéria R. de Arruda2, Taufik Abrão3

1 Federal University of Technology - Paraná (UTFPR), Campus Cornélio Procópio, PR, Brazil 2 Federal University of Technology - Paraná (UTFPR), Campus Curitiba, PR, Brazil

3 State University of Londrina - Paraná (UEL), Londrina, PR, Brazil

## **References**

18 Search Algorithms

Number of Failures: 30 Probability of Success: 0.9970

Number of Failures: 0 Probability of Success: 1.0

Number of Failures: 897 Probability of Success: 0.9103

Number of Failures: 0 Probability of Success: 1.0

Number of Failures: 0 Probability of Success: 1.0

**Author details**

**Table 7.** GA, PSO and DE simulation results for PROC2.

Bruno Augusto Angélico1, Márcio Mendonça1, Lúcia Valéria R. de Arruda2, Taufik Abrão3

**PSO with** P = 10 *A*max 0.7040 0.6590 0.9029 0.9407 0.8134 0.7234 0.6700 0.8153 *A*min 0.6400 0.4130 0.6590 0.6590 0.6590 0.6590 0.6590 0.6590 *A*average 0.6592 0.5006 0.7079 0.7221 0.6809 0.6593 0.6592 0.6850

**GA with** P = 10 *A*max 0.6800 0.5200 0.9038 0.9409 0.7870 0.6700 0.6700 0.8153 *A*min 0.6400 0.4800 0.6590 0.6590 0.6590 0.6590 0.6590 0.6590 *A*average 0.6596 0.5014 0.7500 0.7717 0.7055 0.6596 0.6596 0.7082

**DE with** P = 10 *A*max 0.7523 0.6591 0.9120 0.9477 0.8134 0.7608 0.7530 0.8271 *A*min 0.5747 0.4472 0.6590 0.6590 0.6590 0.6590 0.6590 0.6590 *A*average 0.6597 0.5046 0.7553 0.7808 0.7026 0.6632 0.6632 0.7097

**PSO with** P = 20 *A*max 0.6800 0.5200 0.9040 0.9411 0.7869 0.6700 0.6700 0.8154 *A*min 0.6400 0.4800 0.6590 0.6590 0.6590 0.6590 0.6590 0.6590 *A*average 0.6594 0.5002 0.7116 0.7267 0.6813 0.6594 0.6593 0.6865

**DE with** P = 20 *A*max 0.6800 0.5200 0.9043 0.9427 0.7870 0.6700 0.6700 0.8154 *A*min 0.6400 0.4800 0.6590 0.6590 0.6590 0.6590 0.6590 0.6590 *A*average 0.6595 0.5003 0.7548 0.7827 0.7015 0.6608 0.6608 0.7124

In most scenarios, DE has presented a faster convergence in comparison to PSO and GA, but

1 Federal University of Technology - Paraná (UTFPR), Campus Cornélio Procópio, PR, Brazil

2 Federal University of Technology - Paraná (UTFPR), Campus Curitiba, PR, Brazil

3 State University of Londrina - Paraná (UEL), Londrina, PR, Brazil

the population size needed in DE was higher than the other two methods.

[15] Parsopoulos K.E. and Groumpos P.P. A first study of fuzzy cognitive maps learning using particle swarm optimization. In *IEEE 2003 Congress on Evolutionary Computation*, pages 1440–1447, 2003.

**Chapter 10**

(*i* = *1*, *2*, …, *n*) in series

there are various versions, which are proposed by the

**Optimal Allocation of Reliability in Series Parallel**

One of the most important problems in many industrial applications is the redundancy optimization problem. This latter is well known combinatorial optimization problem where the design goal is achieved by discrete choices made from elements available on the market. The natural objective function is to find the minimal cost configuration of a series-parallel system under availability constraints. The system is considered to have a range of performance levels from perfect working to total failure. In this case the system is called a *multi-state* system

suppliers on the market. Elements are characterized by their cost, performance and availability according to their version. For example, these elements can represent machines in a manufac‐ turing system to accomplish a task on product in our case they represent the whole of electrical power system (generating units, transformers and electric carrying lines devices). Each

elements may be chosen for any given system component. Each component can contain

A limitation can be undesirable or even unacceptable, where only identical elements are used in parallel (i.e. homogeneous system) for two reasons. First, by allowing different versions of the devices to be allocated in the same system, one can obtain a solution that provides the desired availability or reliability level with a lower cost than in the solution with identical parallel devices. Second, in practice the designer often has to include additional devices in the existing system. It may be necessary, for example, to modernize a production line system according to a new demand

contains a number of elements connected in parallel. Different versions of

© 2013 Abdelkader et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Abdelkader et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

(MSS). Let consider a multi-state system containing *n* components *Ci*

levels from customers or according to new reliability requirements.

**Production System**

http://dx.doi.org/10.5772/55725

arrangement. For each component *Ci*

elements of *different* versions as sketched in figure 1.

**1. Introduction**

component *Ci*

Rami Abdelkader, Zeblah Abdelkader, Rahli Mustapha and Massim Yamani

Additional information is available at the end of the chapter


*3.2. Spray-Drying*

In the food industry spray-drying is the most frequently used technique. It is economical, flexible and can be used continuously with easy scale-up [13]. Advantages over the other methods are higher effectiveness and shorter drying time [16]. This technique is based on transforming a material from liquid form into powder form [24]. However, when spray-drying is used for polyphenol encapsulation, conditions must be optimized in order to avoid an accelerated degradation [17]. Depending on process conditions and formulations, spray-drying micrometric capsules can be core-and-shell, multiple core or matrix type [14]. Before spray-drying, a solution or suspension of polyphenols with carriers must be obtained, followed by atomizing into a hot air stream.


**Author details**

Seiko Mitachi1 \*, Ken Satoh2 , Kumiko Shimoyama3 , Makoto Satoh4 and Takeshi Sugiyama5

1 Graduate School of Bionics, Computer and Media Sciences of Tokyo University of Technology, Tokyo, Japan

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Optical Fiber-Based Sleep Apnea Syndrome Sensor DOI: http://dx.doi.org/10.5772/intechopen.91060*