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Biomass supply chain (BSC) activities have caused social and environmental disruptions, such as climate change, energy security issues, high energy demand, and job opportunities, especially in rural areas. Moreover, different economic problems have arisen globally in recent years (e.g., the high costs of BSC logistics and the inefficiency of generating bioenergy from low-energy-density biomass). As a result, numerous researchers in this field have focused on modeling and optimizing sustainable BSC. To this end, this study aims to develop a multi-objective mathematical model by addressing three sustainability pillars (economic cost, environmental emission, and job creation) and three decision levels (i.e., strategic (location of facilities), tactical (type of transportation and routing), and operational (vehicle planning). A palm oil BSC case study was selected in the context of Malaysia in which two advanced evolutionary algorithms, i.e., non-dominated sorting genetic algorithm II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO), were implemented. The study results showed that the highest amounts of profit obtained from the proposed supply chain (SC) design were equal to $13,500 million and $7000 million for two selected examples with maximum emissions. A better target value was achieved in the extended example when 40% profit was reduced, and the minimum emissions from production and transportation in the BSC were attained. In addition, the results demonstrate that more Pareto solutions can be obtained using the NSGA-II algorithm. Finally, the technique for order of preference by similarity to the ideal solution (TOPSIS) was adopted to balance the optimum design points obtained from the optimization algorithm solutions through two-objective problems. The results indicated that MOPSO worked more efficiently than NSGA-II, although the NSGA-II algorithm succeeded in generating more Pareto solutions. [ABSTRACT FROM AUTHOR]

Methaheuristics (MHs) are techniques widely used for solving complex optimization problems. In recent years, the interest in combining MH and machine learning (ML) has grown. This integration can occur mainly in two ways: ML-in-MH and MH-in-ML. In the present work, we combine the techniques in both ways—ML-in-MH-in-ML, providing an approach in which ML is considered to improve the performance of an evolutionary algorithm (EA), whose solutions encode parameters of an ML model—artificial neural network (ANN). Our approach called TS in EA in ANN employs a reinforcement learning neighborhood (RLN) mutation based on Thompson sampling (TS). TS is a parameterless reinforcement learning method, used here to boost the EA performance. In the experiments, every candidate ANN solves a regression problem known as protein structure prediction deviation. We consider two protein datasets, one with 16,382 and the other with 45,730 samples. The results show that TS in EA in ANN performs significantly better than a canonical genetic algorithm (GA in ANN) and the evolutionary algorithm without reinforcement learning (EA in ANN). Analyses of the parameter's frequency are also performed comparing the approaches. Finally, comparisons with the literature show that except for one particular case in the largest dataset, TS in EA in ANN outperforms other approaches considered the state of the art for the addressed datasets. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the laser cladding process parameters of WC/Inconel 625 composite on a low-carbon steel substrate. This process aims to improve industrial parts' wear and corrosion properties in corrosive and high-temperature environments. Laser power, laser cladding speed, powder-feeding rate, and WC content percentage were process input parameters, each at three levels. The dilution ratio and micro-hardness of samples were considered as process responses. Taguchi orthogonal arrays were employed for experimental design, and the results were analyzed using signal-to-noise ratio and analysis of variance. A linear regression model was developed to understand the relationship between process parameters and responses. In addition, two-objective optimization of the process parameters using the second-generation non-dominated sorting genetic algorithm (NSGA-II), and a hybrid algorithm based on the multi-objective whale optimization algorithm (MOWOAGA) were carried out to determine the optimal parameters that minimize the dilution ratio and maximize the cladding hardness. The results showed that the laser power and the powder-feeding rate have the most excellent effect on the dilution ratio. In contrast, WC content percentage and powder-feeding rate strongly affect micro-hardness. Based on the applied meta-heuristic algorithms and confirmed by experimental tests, the laser power of 370 W, laser cladding speed of 4 mm/s, powder-feeding rate of 8 g/min, and WC content percentage of 20.5% are the most optimal process parameters that lead to micro-hardness of 397 Vickers and dilution percentage of 2.5%. This research guides selecting, controlling, and optimizing WC/Inconel 625 composite laser cladding process parameters. [ABSTRACT FROM AUTHOR]

This paper explores the advantages of the Two Degree of Freedom Proportional-Integral-Derivative (2DOF_PID) controller in tracking the reference signal and rejecting the disturbance signal at the same time. Three types of metaheuristic optimization algorithms are employed for tuning the controller's parameters which are Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). These three algorithms have in common that they combine the exploratory concept (global search) and the exploitative concept (local search) in order to reach the optimal global solution. The effectiveness of these algorithms was taken advantage of to improve the performance of the control system that contains the controller. Second and third order plants were adopted for the purpose of testing, evaluating, and comparing the performance of the control system. This aim was accomplished by using each of the optimization algorithms for each plant. The simulation results showed the superiority of the DA in terms of obtaining the lowest value of the Integral Absolute Error (IAE) as the cost function. [ABSTRACT FROM AUTHOR]

The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor's specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization, our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation. Through implementing adaptive protocols according to varying environments and sensor constraints, our study aspires to improve overall network operation. We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization, Particle Swarm Optimization, Genetic Algorithms, and Whale Optimization about performance on real-world problems. Being the most efficient in the optimization process, Biruni displays the lowest error rate at 0.00032. The two other statistical techniques, like ANOVA, are also useful in discovering the factors influencing the nature of sensor data and network-specific problems. Due to the multi-faceted support the comprehensive approach promotes, there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made. Through delivering better performance and reliability for various in-situ applications, this research leads to a fusion of time series forecasters and a customized optimizer algorithm. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

Rapid technological advances have made daily life easier and more convenient in recent years. As an emerging technology, the Internet of Things (IoT) facilitates interactions between physical devices. With the advent of sensors and features on everyday items, they have become intelligent entities able to perform multiple functions as services. IoT enables routine activities to become more intelligent, deeper communication, and processes more efficient. In the dynamic landscape of the IoT, effective service discovery is key to optimizing user experiences. A Quality of Service (QoS)-aware service discovery technique is proposed in this paper to address this challenge. Through whale optimization and genetic algorithms, our method aims to streamline decision-making processes in IoT service selection. The bio-inspired optimization techniques employed in our approach facilitate the discovery of services more efficiently than traditional methods. Our results demonstrate superior performance regarding reduced data access time, optimized energy utilization, and cost-effectiveness through comprehensive simulations. [ABSTRACT FROM AUTHOR]

The inventory routing problem arises from the combination of the vehicle routing problem and the vendor-managed inventory problem. In this paper, we present a mathematical model and a novel genetic algorithm for solving the multiperiod inventory routing problem. The objective is to supply products to scattered customers within a given time horizon while managing customer inventories to avoid shortages and minimize total inventory and transportation costs. To represent solutions for this problem, we introduce a new chromosomal structure. This structure offers simplicity in encoding and decoding solutions, maintains feasibility after crossover and mutation operations, addresses both routing and inventory management in a single step, and consolidates information about each solution method comprehensively. The algorithm parameters, including crossover and mutation rates, population size, number of iterations, and selection pressure, are fine-tuned using the Taguchi method. To assess algorithm efficiency, we utilize standard instances from the literature. Our results demonstrate that the proposed algorithm performs favorably compared to previous approaches. [ABSTRACT FROM AUTHOR]

Given the interest and complexity, of the hybrid flow shops (HFS) problem in industry, he has been extensively considered, the HFS, is a complex combinatorial problem supported in many original world applications. We consider a hybrid flow shop FH4(P3, P2)||Cmax to applied in this paper. In this papers we attempt to optimize the makespan which refers to the last task completion time by an adequate meta-heuristic algorithm based on electromagnetism mechanism (EM). We also present analysis on the performance of the EM-algorithm adapted to HFS scheduling problems. The electromagnetism-like mechanism method gave us efficient and fair results comparing to particle swarm and genetic algorithm. [ABSTRACT FROM AUTHOR]

Today, the use of permanent magnet brushless DC (PMBLDC) motors in vehicles is increasing due to the characteristics of sensorless operation. PMBLDC motor controllers can control the speed and position in the closed-loop feedback system without the need for a position sensor mounted on the shaft. Proportional – Integral – Derivative (PID) controller is one of the most common feedback control algorithms used in PMBLDC motor controllers. Optimizing problems using deterministic methods such as Lagrange and simplex methods requires basic information and complex calculations. Meta-heuristic algorithms are a type of stochastic algorithm that is used to find the optimal solution. Meta-heuristic algorithms are divided into three general categories: evolutionary algorithms, swarm intelligence algorithms, and stochastic algorithms. In this paper, using 14 metaheuristic optimisation algorithms, PID control parameters including settling time, time rise, overshoot, and stability of step response of the mentioned system are optimised. In this paper, 14 meta-heuristic algorithms are simulated and evaluated to optimise the PID coefficients of the controller, including settling time, rising time, excessive increase, and step response stability. The simulation result shows that the genetic algorithm (GA) has the best performance in terms of cost function and biogeography-based optimisation (BBO) in terms of settling time and rising time parameters. Finally, the simulation results are confirmed using experimental results. [ABSTRACT FROM AUTHOR]

The 0–1 Knapsack problem is a non-deterministic polynomial-time-hard combinatorial optimization problem that cannot be solved in reasonable time using traditional methods. Therefore, researchers have turned to metaheuristic algorithms for their ability to solve several combinatorial problems in a reasonable amount of time. This paper adapts the Kepler optimization algorithm using eight V-shaped and S-shaped transfer functions to create a binary variant called BKOA for solving the 0–1 Knapsack problem. Several experiments were conducted to compare the efficacy of the binary Kepler optimization algorithm to several competing optimizers when solving 20 well-known knapsack instances with dimensions ranging from 4 to 75. The experimental results demonstrate the superiority of this algorithm over other metaheuristic algorithms, except for the genetic algorithm, which is marginally superior. To further improve the binary Kepler optimization algorithm, it is combined with an enhanced improvement strategy to create a new hybrid variant. This hybrid variant, termed HBKOA, has superior exploration and exploitation capabilities that make it better than genetic algorithm and other optimizers for all performance metrics considered. The enhanced improvement strategy is also integrated with several competing optimizers, and the experimental results show that HBKOA, hybrid binary manta ray foraging optimization, and hybrid binary equilibrium optimizer are competitive for small and medium-dimensional instances and superior for the higher dimensions. [ABSTRACT FROM AUTHOR]

Background: This research addresses a Vehicle Routing Problem with Simultaneous Delivery and Pickup, Split Loads, and Time Windows (VRPSDPSLTW). In this research, the VRPSDPSLTW problem is adapted for Company X, a shipping company based in Surabaya. The main goal is to enhance the optimal utilization of vessel capacity in the field of shipping transportation and logistics. Little previous research has been done on VRPSDPSLTW at a shipping company. Methods: The optimization approach employed was the Genetic Algorithm (GA), which serves as a metaheuristic to effectively optimize vessel capacity utilization. The algorithm uses One Point Crossover and Swap Mutation operators and analyzes various mutation parameters to determine the best configuration. The GA was coded in R, and experiments were conducted to obtain the best parameter for the GA. Results: The research yielded several outcomes, including route plans, loaded and unloaded Twenty-Foot Equivalent Units (TEUs), travel times, and trip utility from the point of loading (POL) to the point of delivery (POD). In total, there were 85 port visits, surpassing the initial count of 35 ports. Some ports were visited multiple times, with the exception of Surabaya, which served as the home base for a fleet of 15 vessels. The average trip duration was approximately 35 days. Through experimentation, it was determined that employing 1,000 generations along with a mutation probability of 0.2 produces improved solutions. The Genetic Algorithm solution enhanced the average vessel capacity utilization, increasing it to 80.93%. This represents a significant 21.23% increase compared to the global average of 59.7% observed for similar vessel usage scenarios. Conclusions: Furthermore, through the introduction of novel route opportunities, the contributions of each vessel were effectively enhanced. This achievement resulted in an optimal average vessel capacity utilization that met the demand. The findings strongly advocate for the employment of the Genetic Algorithm, highlighting its potential to substantially improve vessel capacity utilization. Consequently, this approach has played a pivotal role in elevating the efficiency of transportation and logistics operations for Company X. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators—the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristic algorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications. [ABSTRACT FROM AUTHOR]

One of the important research issues in wireless sensor networks (WSNs) is the optimal layout designing for the deployment of sensor nodes. It directly affects the quality of monitoring, cost, and detection capability of WSNs. Layout optimization is an NP-hard combinatorial problem, which requires optimization of multiple competing objectives like cost, coverage, connectivity, lifetime, load balancing, and energy consumption of sensor nodes. In the last decade, several meta-heuristic optimization techniques have been proposed to solve this problem, such as genetic algorithms (GA) and particle swarm optimization (PSO). However, these approaches either provided computationally expensive solutions or covered a limited number of objectives, which are combinations of area coverage, the number of sensor nodes, energy consumption, and lifetime. In this study, a meta-heuristic multi-objective firefly algorithm (MOFA) is presented to solve the layout optimization problem. Here, the main goal is to cover a number of objectives related to optimal layouts of homogeneous WSNs, which includes coverage, connectivity, lifetime, energy consumption and the number of sensor nodes. Simulation results showed that MOFA created optimal Pareto front of non-dominated solutions with better hyper-volumes and spread of solutions, in comparison to multi-objective genetic algorithms (IBEA, NSGA-II) and particle swarm optimizers (OMOPSO, SMOPSO). Therefore, MOFA can be used in real-time deployment applications of large-scale WSNs to enhance their detection capability and quality of monitoring. [ABSTRACT FROM AUTHOR]

Channels have various types of cross-sectional shapes, including trapezoidal, rectangular, semi-circular, parabolic, chain-curved, semi-cubic parabolic, egg-shaped, and circular as the most common shapes. A channel designer has many design options in different conditions, including hydraulic, economic, and hydrological conditions, leakage, etc. Among the above-mentioned sections, the first two have a horizontal bottom while the other sections are curve-shaped with bottom curvature. The primary goal in the design of hydraulic channels is to achieve the maximum flow capacity considering the minimum channel construction cost. A variety of studies has been conducted on the different types of hydraulic channels so far, each dealing with the subject from a certain perspective. However, most of the studies have focused on circular, rectangular and trapezoidal channels. This study has focused on the parabolic channel. Genetic algorithm (GA) and particle swarm optimization (PSO) or GRG algorithms and their combination are usually used for optimization. However, this research adopts a novel and updated meta-heuristic algorithm, namely the Harris Hawks Optimization (HHO) algorithm, to optimize the parabolic channel with a fixed roughness coefficient and determine the optimal dimensions of the channel with different flow rates. This channel uses different flow rates, namely 50, 100, 150, 200, 250, and 300 m³/s to solve the optimization problem. Finally, it was found that the lowest construction cost and the highest efficiency for water supply is achieved with a roughness coefficient of 0.015 and a flow rate of 100 m³/s. [ABSTRACT FROM AUTHOR]

In this study, we proposed a new metaheuristic algorithm called Electrical Search Algorithm (ESA). The proposed algorithm is based on the movement of electricity in high-resistive areas such as wood, glass, and gases. ESA has a unique initialization scheme that only one agent initializes at the lower and upper bounds of the search space, which creates structures called poles. After that, ESA uses unique exploration and exploitation strategies to search. The search mechanism is based on electrons moving to opposite poles. ESA differs from other metaheuristics compared to its initialization scheme, pole search mechanism, and update strategy of the best solutions. ESA was tested with the "100-Digit Challenge" benchmark functions in the IEEE-CEC-2019, four well-known benchmark functions, and an np-hard clustering problem. For the clustering problem, we used four well-known datasets: Iris, Wine, Seeds, and Hepatitis C Virus. ESA was compared with seven different metaheuristic algorithms on these well-known benchmark functions, and the results of the clustering problem were compared with the K-Means algorithm. Additionally, Friedman Signed Rank and post hoc Wilcoxon Test were run to show the significance of the results. In all of the well-known benchmark functions, ESA either offered the best results or similar results to other compared algorithms. The score of the ESA on the IEEE-CEC-2019 benchmark functions shows us that even with the minor evaluation numbers, ESA can achieve similar results to the competing algorithms. Results show that ESA has a robust mechanism for not trapping in local points and moves slow but persistent rate. [ABSTRACT FROM AUTHOR]

Traffic planning and management is an important task for governments to achieve a proper functioning of the cities. The configuration of road signs and traffic lights is part of this process. The selection of an appropriate configuration is modeled as a variant of the traffic light cycle optimization problem (TLCOP), which is NP-Hard. To evaluate the impact of each configuration, agent-based simulations can be performed. Based on the importance and complexity of the problem, the objective of this paper is to present and evaluate a metaheuristic scheme to solve it and analyze solutions obtained. The algorithms were applied to six zones of Havana, Cuba. Among all the algorithms used, the genetic algorithm obtained the best performance. [ABSTRACT FROM AUTHOR]

Diabetes is a set of long-term metabolic issues characterized by high blood glucose levels over a drawn-out time. It is a challenging and threatening disease because nearly half of patients who have diabetes do not know that they have it. Data mining techniques have been heavily utilized to predict diabetes in its earliest stage, and therefore can potentially reduce medication costs. Most of the existing research used metaheuristics to optimize neural network parameters or used other data mining algorithms to classify diabetes. This research integrates design of experiments (DOE), genetic algorithm (GA), and multilayer perceptron (MLP) to classify diabetic patients. Two sets of factorial designs were used to determine an optimal set of parameters for the GA. GA is used to optimize the parameters in the MLP, where the MLP acts as a simulator within the GA to provide the fitness evaluation of the solutions. The best resulting solution from DOE–GA–MLP represents the optimized MLP that achieves the highest classification accuracy in diabetes patients. The proposed approach outperforms eight different classification algorithms in terms of accuracy, sensitivity, and F1-score. The developed DOE–GA–MLP model presents a robust predictive tool that can help healthcare professionals identify patients with diabetes in its early stage. Intervention and/or preventive strategies can be implemented in those identified cases to improve patient care and possibly reduce diabetes rates and potential expenditures. [ABSTRACT FROM AUTHOR]

This research aims to show the results of applying optimization in the distribution network of high consumption products, using metaheuristics for the design of distribution routes, applying genetic algorithms, performing the simulation in the HeuristicLab software. Achieving the optimization of the distribution processes of high consumption products which has grown in recent years to the increase of the population and the increase of the vehicular congestion, in the big cities, hindering the achievement of common objectives between the different actors in Urban Distribution of Merchandise (DUM). The research was quantitative cross-sectional non-experimental, because the situations and subjects of study, could not be manipulated by the researcher, the information was provided by the company, which, for reasons of confidentiality, will not be named. [ABSTRACT FROM AUTHOR]

Metaheuristic approaches with significant improvements are very promising in the solution of intractable optimization problems. The objective of the present study is to test the capability of applications and compare performance of the four selected algorithms from "classical" (simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) and "new generation" (firefly algorithm (FFA), krill herd (KH), grey wolf optimization (GWO), and symbiotic organism search (SOS)) each by solving selected benchmark problems. SOS and KH algorithm successfully solved most of all the selected problems by achieving the best solution in minimum execution time. On the other hand, DE, and PSO also effectively attained the optimal solution which were very close to the best one. Therefore, no firm conclusion can be done about the universally best algorithm and their performance may be varied for different benchmark problems. However, "new generation" algorithm exhibited the most promising result and great potential than the "classical" one. This study gives some insights to use SOS and KH as best performing algorithm to the novice user who can easily get lost by the plethora of large number of optimization algorithms. [ABSTRACT FROM AUTHOR]

This article demonstrates the design and implementation of a stable wideband first‐order microwave integrator. The proposed design is attained by the cascading of eight equal‐length serial transmission lines. The meta‐heuristic optimization algorithms namely real‐coded genetic algorithm, particle swarm optimization, and sine–cosine algorithm are applied to attain the characteristic impedance of serial lines. Further, the proposed integrator, fabricated on RT/Duroid 5880 substrate with permittivity (ϵr ${\epsilon }_{r}$) = 2.2 and height (t) $(t)$ = 0.762 mm. The results are measured using a vector network analyzer, show its conformity with the simulated and ideal result within the frequency range of 4–18 GHz in MATLAB environment, and confirm its eligibility for Ku‐band application, which is compact in size. [ABSTRACT FROM AUTHOR]

Job Shop Scheduling (JSS) is one of the problems in the production process. The sequence of operation and processing time are often different because some jobs consist of multiple processes, with each being performed by a different machine. The purpose of JSS is to determine the order in which jobs are processed to meet specific optimization criteria. Several solutions to Job Shop Scheduling Problem (JSSP) have been proposed, either with exact approaches or heuristics. It was observed that one of the most widely used heuristic approaches is metaheuristics, specifically the Genetic Algorithm, which has the advantage of finding solutions globally, also known as global optimal. However, this algorithm is often trapped in a search that only involves local optimal values. This study proposes an approach to improve the Genetic Algorithm performance by combining it with another metaheuristic, known as the Firefly Algorithm, which has the advantage of finding solutions locally or called local optimal. It is, therefore, possible to maintain global and local optimal balance and obtain better performance by combining these two algorithms as well. Furthermore, two approaches are proposed, which include S-GAFA and C-GAFA. Several experiments were performed to measure these proposed algorithms. It was observed that S-GAFA and C-GAFA performed better than the Genetic Algorithm in solving JSSP. [ABSTRACT FROM AUTHOR]

Site road alignment is one of the main elements of architectural environmental design [B. Holdsworth, Refocus 6(1) (2005) 58–60]. It is difficult for traditional methods to achieve both qualitative exploration and quantitative optimization objectives, and optimization algorithms can only optimize quantitative objectives. On the other hand, the shape of the site road is subject to various other human interventions besides the designer, which is a multi-objective problem. Based on the idea of meta-design, this paper proposes a new method for "form-finding" of site roads. This method develops the architecture and components of an expert system, which can handle both qualitative and quantitative objectives in conceptual design, and can realize various human interventions. Component development combines site specification, recursive algorithm, and genetic algorithm, which treats human intervention as a factor of variation, allows for qualitative exploration of different preferences, and finally demonstrates the capabilities of this new approach through case studies. [ABSTRACT FROM AUTHOR]

Due to the growing demand from the civil construction sector around the world, actions minimizing greenhouse gas emissions from this sector have become necessary. Within this scenario, composite steel, and concrete structures have shown to be a good alternative, considering the recyclability of steel and the reduction in the use of concrete. Our study aims to present the formulation of the optimization problem, as well as its application for the composite steel and concrete system with steel deck slabs, aiming at minimizing the CO2 emission of the structure production. To solve the optimization problem, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) were used to verify the convergence of the solutions. Our models were compared with models from the literature to verify the effectiveness of the proposed solution, as well as the impact of the solutions for different classes of concrete strength. The results show that the best solution from the environmental point of view will not always be the best solution from the economic point of view and that the algorithms were efficient in determining the optimal solutions. [ABSTRACT FROM AUTHOR]

Polyphase filter banks (PFBs) are the most preferred multirate structures for subband coding in Digital Signal Processing (DSP) and communication. For PFB design, there are many important design parameters such as filter length and frequency selectivity. Also, to realize the desired frequency response in designs, stopband and passband attenuation are of considerable importance. In PFB design, researchers and practitioners frequently use iterative and meta-heuristic optimization methods. Heuristic techniques have a significant problem-solving ability in continuous and discrete solution space. Therefore, they give better results than other suggested methods, and their performance depends on the control parameters. In this study, Artificial Bee Colony (ABC) algorithm was employed for suggested design problem of PFB. In the first stage, the control parameters of the ABC algorithm were examined to improve the performance of the proposed PFB problem. In the second stage, the analysis was carried out by changing filter lengths (8-256) and filter band frequencies (0.3-0.7/0.4-0.6). All results obtained were also compared with the Particle Swarm Optimization algorithm (PSO) and the Genetic algorithm (GA). Finally, a DSP application of PFB was carried out according to best results achieved by the ABC algorithm for filter lengths and frequencies. [ABSTRACT FROM AUTHOR]

Scalability of optimization algorithms is a major challenge in coping with the ever-growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly, population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird’s-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part I of the series covers two major algorithmic approaches to large-scale global optimization: 1) problem decomposition and 2) memetic algorithms. Part II of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally, touches upon the pitfalls and challenges of current research and identifies several potential areas for future research. [ABSTRACT FROM AUTHOR]

• Porous microstructure dictates the overall performance of a reactor. • Ractor microstructure is optimized by pore network modeling and metaheuristic optimization. • A multi-objective optimization led to a higher conversion rate and a lower hydraulic requirement. • The idea of background grid is presented for a versatile topological optimization of the pore network. Reactive transport within porous reactors is crucial to many diverse applications, and the efficacy of these reactors hinges on their microstructure. Mathematical modeling and optimization play a pivotal role in the exploration of efficient designs, enabling the generation of structures that may not be achievable through random realizations of packings. In this study, we propose a framework for high-resolution topological optimization of porous flow-through reactors based on pore-scale simulations using a non-dominated sorting genetic algorithm II. A pore network model for an advection–diffusion-reaction system is developed to simulate reactor performance. This model is integrated with a mathematical optimization algorithm, incorporating a background grid and Delaunay tessellation. The optimization framework generates enhanced porous structures, simultaneously maximizing conversion rates while minimizing pumping costs. Striking a balance between permeability and reactive surface area, the final designs yield a set of Pareto optimal solutions, encompassing diverse non-dominated designs with varying reaction rates and hydraulic requirements. The results demonstrate that optimal pore configurations lead to a 280% increase in conversion rates and a 6% reduction in pumping costs at one end, while on the opposite end of the Pareto front, a 15.2% increase in reaction rates and an 11.3% reduction in pumping costs are observed. [ABSTRACT FROM AUTHOR]

The growing need for profit maximization and cost minimization has made the optimization field very attractive to both researchers and practitioners. In fact, many authors were interested in this field and they have developed a large number of optimization algorithms to solve either academic or real-life problems. Among such algorithms, we cite a well-known metaheuristic called the genetic algorithm. This optimizer tool, as any algorithm, suffers from some drawbacks; like the problem of premature convergence. In this paper, we propose a new selection strategy hoping to avoid such a problem. The proposed selection operator is based on the principle of the k-means clustering method for the purpose of guiding the genetic algorithm to explore different regions of the search space. We have elaborated a genetic algorithm based on this new selection mechanism. We have then tested our algorithm on various data instances of the 0/1 multidimensional knapsack problem. The obtained results are encouraging when compared with those reached by other versions of genetic algorithms and those reached by an adapted version of the particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]

Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based on the number of optimized objective functions: single objective and multi objective optimization problems. In this paper, a comparative review since 2000 using one of the deterministic and stochastic modelling approaches called goal programming (GP) and genetic algorithm (GA) in multi-objective optimization problem is discussed. This study gives a prime review of the application of GP and GA in various criteria of project portfolio selection problem. GP is a method for solving large-scale multiobjective optimization problems to assist decision makers in finding solutions that satisfy several competing goals. GA on the other hand are global meta-heuristic search algorithms that are used to provide approximation or optimal solutions to large-scale optimization problems. Of the 23 articles considered in this review showed that, from more than 100 projects, GA proved near optimal, feasible solution and efficient frontier in projects ranking, projects interaction and a preferred decision support tool of project portfolio selection. In addition, the two models select projects on risk-based approach, but GA proved to be more effective in terms of number of projects proposed, central processing unit (CPU) time and accuracy. The review concludes that, in multiobjective optimization model for project portfolio selection problems on a large-scale, very large or complex problems and less CPU time, GA is more effective than GP in multi-objective optimization problems. The review also showed gaps in previous studies of GP and GA application on project portfolio selection problem (PPSP). This review will aid scholars and demanding practitioners in gaining a broader understanding of goal programming and genetic algorithms in the context of project portfolio selection problems. [ABSTRACT FROM AUTHOR]

This work presents numerical optimization algorithm based on genetic algorithm to solve an inverse problem to reconstruct the Robin coefficient in boundary value problem. It consists of identifying the Robin coefficient on the inaccessible part of the boundary representing the corrosion damage of some specimen material. This problem is known to be severely ill-posed in Hadamard sense. Metaheuristics are methods inspired by natural phenomena which have shown their effectiveness in solving several optimization problems in different domains. Thus, adapted genetic operators for real coded genetic algorithm is proposed by formulating the problem into an optimisation one. Numerical results are presented to illustrate and evaluate the efficiency and the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Some species of females, e.g., chicken, bird, fish etc., might mate with more than one males. In the mating of these polygamous creatures, there is competition between males as well as among their offspring. Thus, male reproductive success depends on both male competition and sperm rivalry. Inspired by this type of sexual life of roosters with chickens, a novel nature-inspired optimization algorithm called Roosters Algorithm (RA) is proposed. The algorithm was modelled and implemented based on the sexual behavior of roosters. 13 well-known benchmark optimization functions and 10 IEEE CEC 2018 test functions are utilized to compare the performance of RA with the performance of well-known algorithms; Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Grey Wolf Optimizer (GWO). Also, non-parametric statistical tests, Friedman and Wilcoxon Signed Rank Tests, were performed to demonstrate the significance of the results. In 20 of the 23 functions that were tested, RA either offered the best results or offered similar results to other compared algorithms. Thus, in this paper, we not only present a novel nature-inspired algorithm, but also offer an alternative method to the well-known algorithms commonly used in the literature, at least as effective as them. [ABSTRACT FROM AUTHOR]

This study presents the application of three recent metaheuristic optimization algorithms to design the tuned mass damper (TMD) for a high-rise building structure, taking into account the effect of soil–structure interaction (SSI) in the frequency domain. The algorithms used in this study were grey wolf optimizer (GWO), moth–flame optimization (MFO), and whale optimization algorithm (WOA), and the obtained results were compared with the well-known particle swarm optimization (PSO) and genetic algorithm (GA). The TMD optimization problem was formulated with two objective functions: the H2 and the H∞ norms of the top floor displacement. The frequency, the mass, and the damping ratio of the TMD were optimized within defined bounds for a 40-story benchmark structure. Unlike in previous research, the optimization in this study was performed for different mass ratios. First, the efficiency of the obtained optimum parameters was plotted in the frequency domain and then tested in the time domain by presenting the top floor displacement of the same benchmark structure. The time domain analysis was carried out using the El Centro and Petrolia earthquakes as external disturbances. It was found that the optimal TMD parameters are affected by the soil type. The GWO algorithm reached the optimal solutions faster and more efficiently than the other algorithms. GA limitations were pointed out. [ABSTRACT FROM AUTHOR]

Many real-life optimization problems in different fields of engineering, science, business, and economics are challenging to solve, due to the complexity and as such they are classified as non-deterministic polynomial-time hard. In recent years, nature-inspired metaheuristics are proved to be robust solvers for global optimization problems. Hybridization is a commonly used technique that can further improve metaheuristic algorithms. Hybrid algorithms are designed by combining the advantages of various algorithms, which produce a synergistic effect. Hybridization results in intensifying specific advantages in different algorithms and the hybridized algorithm implementation often performs better than the original version. In this paper, we present the hybridized artificial flora optimization algorithm, named genetically guided best artificial flora. This hybridization is achieved by using a uniform crossover and mutation operators from the genetic algorithms that facilitate exploration of the search space and make the right balance between diversification and intensification. Furthermore, the proposed hybrid algorithm is adopted for two real-world problems, artificial neural network training, and feature selection problem. Following good practice, proposed method was first tested on standard unconstrained functions before it was evaluated for these two very important machine learning challenges. The experimental results show that the proposed hybridized algorithm is highly competitive and that it establishes a better balance between exploration and exploitation than the original one and that it is superior over other state-of-the-art methods in artificial neural network training and feature selection. [ABSTRACT FROM AUTHOR]

The most efficient artificial intelligence (AI) and metaheuristic algorithms have been assessed for the design of nanovector (NV) drug delivery systems. This was done through a thorough search of modeling and optimization algorithms for nanoparticles. Adaptive neuro-fuzzy inference system (ANFIS), a modeling algorithm not currently used for NV drug delivery systems, was compared to the most used algorithm, multilayer perceptron artificial neural network (MLP-ANN), by modeling a dataset of nanovectors. ANFIS provided consistently a better modeling than MLP-ANN for nanovector data regarding particle size, polydispersity index, zeta potential, and drug loading. Similarly, optimization algorithms not currently used for NV drug delivery systems, cuckoo search (CS), symbiotic organism search (SOS), and firefly algorithm (FA), were compared to genetic algorithm (GA), the most used algorithm, by optimizing several benchmark functions. CS and SOS outperformed in function optimization both GA and FA. Comparison among AI or metaheuristic algorithms is scarce. Our results show that current most used NV drug delivery systems AI and metaheuristic algorithms are not the most efficient for modeling and optimization of physicochemical properties of NVs. Implementation of algorithms here suggested, in addition to sample size calculations, should yield better predictions to optimize NV properties. [ABSTRACT FROM AUTHOR]

In this article, we study an Inventory Routing Problem with deterministic customer demand in a two-tier supply chain. The supply chain network consists of a supplier using a single vehicle with a given capacity to deliver a single product type to multiple customers. We are interested in population-based algorithms to solve our problem. A Memetic Algorithm (MA) is developed based on the Genetic Algorithm (GA) and Variable Neighborhood Search methods. The proposed meta-heuristics are tested on small and large reference benchmarks. The results of the MA are compared to those of the classical GA and to the optimal solutions in the literature. The comparison shows the efficiency of using MA and its ability to generate high quality solutions in a reasonable computation time. [ABSTRACT FROM AUTHOR]

In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classification. MFO is inspired by the behavior of moths with respect to flames, which is the navigation method of moths in nature called transverse orientation. In MFO, a moth navigates the search space through a process called transverse orientation by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering that the Moon's distance from the moth is considerably long. Our research tested MFO on three benchmark hyperspectral datasets—Indian Pines, University of Pavia, and Salinas. MFO produced an Overall Accuracy (OA) of 88.98%, 94.85%, and 97.17%, respectively, on the three datasets. Our experimental results indicate that MFO produces better OA and Kappa when compared to state-of-the-art band selection algorithms such as particle swarm optimization, grey wolf, cuckoo search, and genetic algorithms. The analysis results prove that the proposed approach effectively addresses the spectral band selection problem and provides a high classification accuracy. [ABSTRACT FROM AUTHOR]

Reserve evaluation is a very difficult and complex process. The most important and yet most challenging part of this process is grade estimation. Its difficulty derived from challenges in obtaining required data from the deposit by drilling boreholes, which is a very time-consuming and costly act itself. Classic methods which are used to model the deposit are based on some preliminary assumptions about reserve continuity and grade spatial distribution which are not true about all kind of reserves. In this paper, a multilayer perceptron (MLP) artificial neural network (ANN) is applied to solve the problem of ore grade estimation of highly sparse data from Zarshouran gold deposits in Iran. The network is trained using four metaheuristic algorithms in separate stages for each algorithm. These algorithms are artificial bee colony (ABC), genetic algorithm (GA), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO). The accuracy of predictions obtained from each algorithm in each stage of experiments was compared with real gold grade values. We used unskillful value to check the accuracy and stability of each network. Results showed that the network trained with the ABC algorithm outperforms other networks that trained with other algorithms in all stages having the least unskillful value of 13.91 for validation data. Therefore, it can be more suitable for solving the problem of predicting ore grade values using highly sparse data. [ABSTRACT FROM AUTHOR]

• Study of PV/Biomass/Battery system for a remote village in Bangladesh. • Probabilistic modeling to address the uncertainties of resources and load demand. • Optimization with three different algorithms for effective global minima finding. • Performance superiority of Dandelion Optimizer (DO). • Obtained results provide technical and economic margins for stakeholders. Hybrid Renewable Energy System (HRES) has become a popular alternative for locations restricted to national grid connection due to geographical limitations. The study investigates the available renewable resources of a remote village in Mymensingh district of Bangladesh to propose and evaluate optimal sizing and cost of a grid independent HRES. The intermittency of solar irradiance and uncertain variation in load demand are taken into account by adopting probabilistic scenario-based analysis (SBA). Beta distribution and Gaussian distribution are considered for solar irradiance and load uncertainties, respectively, and Roulette Wheel mechanism generates multiple probabilistic scenarios. Objective function is formulated to minimize the total system cost (TSC) under defined constraints. Dandelion optimizer (DO), a relatively recent and unexplored metaheuristic algorithm along with two other popular optimization algorithms, Slime Mould Algorithm (SMA) and Real Coded Genetic Algorithm (RCGA) are applied to size the components for different probabilistic scenarios generated by Roulette Wheel. DO outperformed SMA and RCGA providing the solution set with 18.4 % and 3.2 % reduction in simulation time and system cost, respectively, in comparison with RCGA, whereas, 11.3 % and 16.7 % reduction in simulation time and system cost, respectively, in contrast to SMA. DO's computation of cost margin (1.17 million dollars to 1.46 million dollars), PV allocation (minimum 7 modules to maximum 218 modules), biomass power allocation (maximum of 70.34215 kW) and optimized battery allocation computed by the algorithms (24 units) provide authorities a margin of cost-efficient estimation with conservative preparedness for developing HRES in the selected region. The study also provides a scientific framework to account for the uncertain parameters while proposing and analyzing HRES for off-grid localities, where inadequate literary exploration is evident in the context of Bangladesh. [ABSTRACT FROM AUTHOR]

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone. [ABSTRACT FROM AUTHOR]

Summary: Convolutional neural networks (CNN) are deep learning models widely reported as performing well in several image classification tasks. Training the networks for optimal performance is considered an NP‐hard problem. The combination of a metaheuristic algorithm with CNN has been proposed to address this problem because the metaheuristic algorithms have efficient performance, parameter tuning, and stagnation prevention methods. Moreover, there is now an increasing need to apply the best performing metaheuristic algorithm to optimize the parameters, training and learning rate of the CNN models. This study aims to investigate the best performing metaheuristic algorithm for fine‐tuning the weights, biases, and hyperparameters of CNN networks for solving the problem of characterization of abnormalities in breast images. Furthermore, hybrid models consisting of a CNN architecture and five representative metaheuristic algorithms to efficiently detect breast cancer abnormalities are presented. The adopted approach involves training a CNN network using genetic algorithm (GA), whale optimization algorithm (WOA), multiverse optimizer (MVO), satin bower optimization (SBO), and life choice‐based optimization (LCBO) algorithms to optimize only weights and bias of the model. Two categories of experiments were carried out: the first involved training of the CNN model, while the second involved optimizing the training of CNN model with metaheuristic algorithms. A comparative analysis of the impact of the proposed GA, WOA, MVO, SBO, and LCBO metaheuristic algorithms on the performance of the CNN architecture was carried out. Empirical and statistical analyses are presented to validate further the findings obtained in the study. Results obtained showed that MVO, SBO and LCBO outperformed GA, WOA. The classification accuracy obtained for CNN‐GA, CNN‐WOA, CNN‐MVO, CNN‐SBO, and CNN‐LCBO at the fifth epoch was 0.76, 0.75, 0.84, 0.80, and 0.86, respectively. At the same time, the traditional CNN achieved an accuracy of 0.66. The outcome of this study showed that contrary to widespread practice, physics‐based, biology‐based, and human‐based optimization algorithms promise better performance parameter tuning for CNN as compared with evolutionary‐based and swarm‐based metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Researchers from different domains have developed several metaheuristic algorithms that are inspired by the biological phenomenon. In this work, we propose a novel algorithm that is inspired by the prey–predator interaction of animals. In the proposed algorithm, two random populations are considered as a predator and preys. The energy gain for both predators and preys calculated based on their body mass and the interaction between predators and their mutual preys. The best predator (i.e., predator with the highest energy gain) performs a local search (exploitation). The other solution members in prey population facilitate the exploration of the search space. The proposed algorithm is evaluated by three phases. Firstly, a set of 16 mathematical functions are considered to test different characteristics of predator–prey optimization (PPO) algorithm as a optimizer. Secondly, it is evaluated by seven datasets as a solver for feature selection problem. The proposed algorithm as an global optimizer has been compared with other metaheuristic algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), ant lion optimizer (ALO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), differential evolution (DE), sine–cosine algorithm (SCA), moth flame optimization (MFO), and salp swarm algorithm (SSA). The results indicate that PPO improves convergence rate by 33.8%, 24.2%, 37.3%, 10.5%, 12.4%, 22.7%, 18.6%, 30.7%, and 29.1% in average compared to GA, PSO, ALO, GWO, WOA, DE, SCA, MFO, and SSA, respectively. Furthermore, PPO obtains a higher Performance Index around 5–10% than competitor approaches with respect to exploration–exploitation balance and a high degree of stability. Secondly, the performance of PPO for feature selection issues demonstrates the applicability PPO algorithm in solving real problems with unknown search spaces. [ABSTRACT FROM AUTHOR]

The use of metaheuristic optimization techniques in obtaining the optimal weights of neural network model for the time series was the main part of this research. The three optimization methods used as experiments were genetic algorithm (GA), particle swarm optimization (PSO), and modified bee colony (MBC). Feed forward neural network (FFNN) was the neural network (NN) architecture chosen in this research. The limitations and weaknesses of gradient-based methods for learning algorithm inspired some researchers to use other techniques. A reasonable choice is non-gradient based method. Neural network is inspired by the characteristics of creatures. Therefore, the optimization techniques which are also resemble the patterns of life in nature will be appropriate. In this study, various scenarios on the three metaheuristic optimization methods were applied to get the best one. The proposed procedure was applied to the rainfall data. The experimental study showed that GA and PSO were recommended as optimization methods at FFNN model for the rainfall data. [ABSTRACT FROM AUTHOR]

Metaheuristics are widely applied to solve optimization problems. Numerous metaheuristic algorithms inspired by natural processes have been introduced in the past years. Studying and comparing the convergence of metaheuristics is helpful in future algorithmic development and applications. This study focuses on bee-inspired metaheuristics and identifies seven basic or root algorithms applied to solve continuous optimization problems. They are the bee system, mating bee optimization (MBO), bee colony optimization, bee evolution for genetic algorithms (BEGA), bee algorithm, artificial bee colony (ABC), and bee swarm optimization. The algorithms' performances are evaluated with several benchmark problems. This study's results rank the cited algorithms according to their convergence efficiency. The strengths and shortcomings of each algorithm are discussed. The ABC, BEGA, and MBO are the most efficient algorithms. This study's results show the convergence rate among different algorithms varies, and evaluates the causes of such variation. [ABSTRACT FROM AUTHOR]

Feature transformation methods can be used effectively to improve the performance of the machine learning techniques, such as classification and clustering, via enhancing the discrimination among the samples. Additionally, binary representation of features may be necessary in several circumstances. For example, distributed systems may have limited bandwidth, storage, and energy that necessitate rough quantization of the measurements. This paper proposes a random projection-based feature transformation method that maps the data points from the original space into a new binary space. In the proposed transformation method, the random projection process is formulated as an optimization problem. A modified chaotic version of the sine–cosine algorithm is used to find the optimal random projector. The performance of the proposed transformation method is validated using 15 standard UCI benchmark datasets against the linear discernment analysis. Moreover, it is compared to other metaheuristic-based feature reduction methods including the gray wolf optimizer, the genetic algorithm, and the antlion optimizer. The obtained results assure the superiority of the proposed transformation method in terms of classification accuracy and storage requirements. [ABSTRACT FROM AUTHOR]

In this manuscript, the gain of the EDFA-Raman hybrid optical amplifier (HOA) is maximized using a popular nature inspired Whale Optimization Algorithm (WOA). The dominating parameters of HOA, length of Raman amplifier and EDFA and its pump powers are optimized to get the maximum possible gain. Further, the performance of WOA is compared by using other metaheuristic techniques such as Real coded Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, to optimize the same model of HOA. WOA proves to be a better optimization technique as it is able to find maximum gain, have a better convergence curve and box plot performance as compared to the other three techniques. Moreover, the gain flatness and noise figure performance of HOA is compared with EDFA and Raman amplifier. [ABSTRACT FROM AUTHOR]

In this work, a study case is presented in which the design of the layout for a CMOS sensor cell is partially automated by implementing a metaheuristic algorithm to find the best tradeoff between two conflicting objectives (two quantitative opposite and not totally independent yet desired performance or design qualities) among the set of feasible layout and electronic device configurations within a constricted search space. The feasibility of a solution (a particular configuration) and its capability to fulfill every requested objective, is determined by its compliance to the CMOS-MEMS design rules and fabrication process. Any given solution besides showing optimal or very near-to-the-optimal characteristics, must be suitable to be fabricated in the CMOS conventional process which for this case is a 0.5 μ m , 3-metal 2-poly N-well fabrication, beside this, since monolithic inertial sensors generally contains embedded movable electromechanical parts a surface micromachining must be considered. Simulation data and behavior of the bio-inspired metaheuristic algorithm used during the design process are presented, as well as electromechanical simulation results based the automatic-generated solutions. [ABSTRACT FROM AUTHOR]

In manufacturing-cell-formation research, a major concern is to make groups of machines into machine cells and parts into part families. Extensive work has been carried out in this area using various models and techniques. Regarding these ideas, in this paper, experiments with varying parameters of the popular metaheuristic algorithm known as the genetic algorithm have been carried out with a bi-criteria objective function: the minimization of intercell moves and cell load variation. The probability of crossover (A), probability of mutation (B), and balance weight factor (C) are considered parameters for this study. The data sets used in this paper are taken from benchmarked literature in this field. The results are promising regarding determining the optimal combination of the genetic parameters for the machine-cell-formation problems considered in this study. [ABSTRACT FROM AUTHOR]