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In linear registration, a floating image is spatially aligned with a reference image after performing a series of linear metric transformations. Additionally, linear registration is mainly considered a preprocessing version of nonrigid registration. To better accomplish the task of finding the optimal transformation in pairwise intensity-based medical image registration, in this work, we present an optimization algorithm called the normal vibration distribution search-based differential evolution algorithm (NVSA), which is modified from the Bernstein search-based differential evolution (BSD) algorithm. We redesign the search pattern of the BSD algorithm and import several control parameters as part of the fine-tuning process to reduce the difficulty of the algorithm. In this study, 23 classic optimization functions and 16 real-world patients (resulting in 41 multimodal registration scenarios) are used in experiments performed to statistically investigate the problem solving ability of the NVSA. Nine metaheuristic algorithms are used in the conducted experiments. When compared to the commonly utilized registration methods, such as ANTS, Elastix, and FSL, our method achieves better registration performance on the RIRE dataset. Moreover, we prove that our method can perform well with or without its initial spatial transformation in terms of different evaluation indicators, demonstrating its versatility and robustness for various clinical needs and applications. This study establishes the idea that metaheuristic-based methods can better accomplish linear registration tasks than the frequently used approaches; the proposed method demonstrates promise that it can solve real-world clinical and service problems encountered during nonrigid registration as a preprocessing approach.The source code of the NVSA is publicly available at https://github.com/PengGui-N/NVSA. [ABSTRACT FROM AUTHOR]

This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications. [ABSTRACT FROM AUTHOR]

This article develops an evolutionary nature inspired algorithm based on the social behavior of the goat, a pet of a farmer in a village life. In village life, we generally see the shepherds keep their goats free/untie from collar thread for grazing in the early morning and receives them at the end of the day when they come back into the home with their own efforts. But some day the goats did not come back in due time because of overfeeding of grass causing unable to move any more after meeting their grasp and began to get rest there. The shepherd feels more tempted and began to search for his/her goat. After untie, the goat began to graze herself through the walk on the path of the cultivated land and bank of the village ponds. The search process is going on through that path until it is not finally got. To characterize this problem some definitions like false walk, uniform and non-uniform steps, goat's jump, periodic walk and goodness of fit for various walk functions have been discussed here rigorously. Inspiring from this fact novel metaheuristic algorithms along with pseudocode and hardware specification have been discussed to optimize a benchmark multi-modal objective function having some singularity zones explicitly. Numerical results have been compared with some of the existing state- of -arts under 95% confidence intervals. Also, graphical illustrations are performed to validate the proposed approach. Finally, a conclusion is made followed by scope of future work. [ABSTRACT FROM AUTHOR]

Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that simulates Archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to prevent being trapped in local optima. Despite the wide acceptance of AOA, it has some drawbacks, such as the assumption that individuals modify their locations depending on altered densities, volumes, and accelerations. This causes various shortcomings such as stagnation into local optimal regions, low diversity of the population, weakness of exploitation phase, and slow convergence curve. Thus, the exploitation of a specific local region in the conventional AOA may be examined to achieve a balance between exploitation and exploration capabilities in the AOA. The bird Swarm Algorithm (BSA) has an efficient exploitation strategy and a strong ability of search process. In this study, a hybrid optimizer called AOA-BSA is proposed to overcome the limitations of AOA by replacing its exploitation phase with a BSA exploitation one. Moreover, a transition operator is used to have a high balance between exploration and exploitation. To test and examine the AOA-BSA performance, in the first experimental series, 29 unconstrained functions from CEC2017 have been used whereas the series of the second experiments use seven constrained engineering problems to test the AOA-BSA's ability in handling unconstrained issues. The performance of the suggested algorithm is compared with 10 optimizers. These are the original algorithms and 8 other algorithms. The first experiment's results show the effectiveness of the AOA-BSA in optimizing the functions of the CEC2017 test suite. AOABSA outperforms the other metaheuristic algorithms compared with it across 16 functions. The results of AOABSA are statically validated using Wilcoxon Rank sum. The AOABSA shows superior convergence capability. This is due to the added power to the AOA by the integration with BSA to balance exploration and exploitation. This is not only seen in the faster convergence achieved by the AOABSA, but also in the optimal solutions found by the search process. For further validation of the AOABSA, an extensive statistical analysis is performed during the search process by recording the ratios of the exploration and exploitation. For engineering problems, AOABSA achieves competitive results compared with other algorithms. the convergence curve of the AOABSA reaches the lowest values of the problem. It also has the minimum standard deviation which indicates the robustness of the algorithm in solving these problems. Also, it obtained competitive results compared with other counterparts algorithms regarding the values of the problem variables and convergence behavior that reaches the best minimum values. [ABSTRACT FROM AUTHOR]

This paper introduces a novel swarm-based metaheuristic called swarm magnetic optimizer (SMO). SMO imitates the behaviour of two magnets close to each other: pushing toward or pulling away from each other. This pushpull mechanism is then adopted to become a novel search in SMO. SMO is set up as a swarm of magnets that move autonomously. In the early iteration, the pull strategy is dominant. Meanwhile, it declines during the iteration and is replaced by a push strategy. The determination between these two strategies is calculated stochastically. SMO consists of three sequential phases in every iteration where the corresponding magnet and its reference run a push or pull search in each phase. The reference used in each phase is the global best magnet, a randomly selected magnet, and a randomly generated magnet. This paper evaluates SMO through simulation to find the optimal solution for 23 functions. The performance of SMO is compared with five latest metaheuristics: mixed leader-based optimizer (MLBO), guided pelican algorithm (GPA), zebra optimization algorithm (ZOA), coati optimization algorithm (COA), and clouded leopard optimizer (CLO). The result shows that SMO is better than MLBO, GPA, ZOA, COA, and CLO in consecutively 22, 15, 11, 13, and 14 functions. The superiority of SMO, especially, is in solving high dimension functions. Meanwhile, in the fixed-dimension multimodal functions, SMO is still superior to MLBO but less superior to GPA, ZOA, COA, and CLO. [ABSTRACT FROM AUTHOR]

The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g., ETSI) provide requirements in terms of maximum transmit power but do no mandate specific radar waveform parameters nor channel access scheme policies. Techniques for interference mitigation are thus becoming very important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. In our previous work, we have shown that organizing the radar band into time-frequency resources that do not interfere with each other vastly reduces the amount of interference by facilitating band sharing. In this paper, a metaheuristic is presented to find the optimal resource sharing between radars, knowing their relative positions and thereby the line-of-sight and non-line-of-sight interference risks during a realistic scenario. The metaheuristic aims at optimally minimizing interference while minimizing the number of resource changes that radars have to make. It is a centralized approach where everything about the system is known (e.g., the past and future positions of the vehicles). This and the high computational load induce that this algorithm is not meant to be used in real-time. However, the metaheuristic approach can be extremely useful for finding near optimal solutions in simulations, allowing for the extraction of efficient patterns, or as data generation for machine learning. [ABSTRACT FROM AUTHOR]

This paper presents a new evolutionary-based approach called a Subtraction-Average-Based Optimizer (SABO) for solving optimization problems. The fundamental inspiration of the proposed SABO is to use the subtraction average of searcher agents to update the position of population members in the search space. The different steps of the SABO's implementation are described and then mathematically modeled for optimization tasks. The performance of the proposed SABO approach is tested for the optimization of fifty-two standard benchmark functions, consisting of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results show that the proposed SABO approach effectively solves the optimization problems by balancing the exploration and exploitation in the search process of the problem-solving space. The results of the SABO are compared with the performance of twelve well-known metaheuristic algorithms. The analysis of the simulation results shows that the proposed SABO approach provides superior results for most of the benchmark functions. Furthermore, it provides a much more competitive and outstanding performance than its competitor algorithms. Additionally, the proposed approach is implemented for four engineering design problems to evaluate the SABO in handling optimization tasks for real-world applications. The optimization results show that the proposed SABO approach can solve for real-world applications and provides more optimal designs than its competitor algorithms. [ABSTRACT FROM AUTHOR]

This work introduces a new metaheuristic designed to solve high-dimension problems, namely multiple interaction-dual leader optimizer (MIDLO). There are two novel mechanics regarding this proposed metaheuristic. First, there are multiple interactions and movements during the guided search carried out by every agent in every iteration. Second, there are two references in every interaction: the global best solution and a randomly chosen solution. Meanwhile, a random search within the space is carried out by any agent that fails to improve its own solution after several consecutive trials. In this work, MIDLO is tested to solve 23 classic functions so that its performance can be evaluated. In this test, MIDLO is competed with five swarm-based metaheuristics: particle swarm optimization (PSO), stochastic marine predator algorithm with multiple candidates (SMPA-MC), golden search optimizer (GSO), average and subtraction-based optimizer (ASBO), and guided pelican algorithm (GPA). The result indicates that MIDLO is better than these sparing metaheuristics, especially in optimizing the high-dimension functions. Overall, MIDLO is better than PSO, SMPA-MC, GSO, ASBO, and GPA in solving 20, 12, 15, 9, and 13 functions respectively. The result also indicates that the number of interactions has a positive relation with the performance while the effect of a maximum number of lives depends on the problem to solve. [ABSTRACT FROM AUTHOR]

This paper proposes a novel swarm intelligence-based metaheuristic called as sea-horse optimizer (SHO), which is inspired by the movement, predation and breeding behaviors of sea horses in nature. In the first two stages, SHO mimics different movements patterns and the probabilistic predation mechanism of sea horses, respectively. In detail, the movement modes of a sea horse are divided into floating spirally affected by the action of marine vortices or drifting along the current waves. For the predation strategy, it simulates the success or failure of the sea horse for capturing preys with a certain probability. Furthermore, due to the unique characteristic of the male pregnancy, in the third stage, the proposed algorithm is designed to breed offspring while maintaining the positive information of the male parent, which is conducive to increase the population diversity. These three intelligent behaviors are mathematically expressed and constructed to balance the local exploitation and global exploration of SHO. The performance of SHO is evaluated on 23 well-known functions and CEC2014 benchmark functions compared with six state-of-the-art metaheuristic algorithms. Finally, five real-world engineering problems are utilized to test the effectiveness of SHO. The experimental results demonstrate that SHO is a high-performance optimizer and positive adaptability to deal with constraint problems. SHO source code is available from: https://www.mathworks.com/matlabcentral/fileexchange/115945-sea-horse-optimizer [ABSTRACT FROM AUTHOR]

This study proposes a new metaheuristic algorithm, which is called Artificial Locust Swarm Optimization (ALSO), inspired by random jumping and plant invasion behavior of locust swarms. Locusts interact in two different ways of searching for food: social and familial. In the familial phase, small locust groups search foods in a local area and the locusts share their information in the social phase. The proposed algorithm is less likely to trap into the local solution than other methods and has high performance in the sensitivity of the global solution. In addition, it is effective not only for the solution of black-box optimization problems but also for the solution of problems with an irregular objective function. The ALSO algorithm is compared with other recent and well-known optimization algorithms on 22 benchmark functions and 3 real engineering design problems. Simulation results prove that the ALSO algorithm is very competitive when compared to the other algorithms. Moreover, it even requires the less runtime and memory space under the same conditions. [ABSTRACT FROM AUTHOR]

This paper introduces a new metaphor-free metaheuristic called attack-leave optimizer (ALO). As the name suggests, ALO deploys two strategies to find the optimal solution. The central concept of ALO is to intensify guided searches as a required method. Then, a random search is performed only if the guided search fails to improve the current solution. ALO consists of four guided searches and one random search, performed in three phases: two mandatory and one optional. In the first phase, the guided search is conducted with the best global solution as the reference. In the second phase, the guided search is conducted with a randomly selected solution as the reference. The random search is performed in the third phase. Evaluating ALO, it was tested on 23 classic functions and benchmarked against five existing metaheuristics with known shortcomings: Mixed leader-based optimizer (MLBO), slime mould algorithm (SMA), golden search optimizer (GSO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA). The results indicate that ALO is highly competitive, outperforming MLBO, SMA, GSO, COA, and ZOA in solving 16, 16, 14, 10, and 9 functions respectively, and demonstrating ALO as a promising new metaheuristic. [ABSTRACT FROM AUTHOR]

In fake news detection, intelligent optimization seems to be a more effective and explainable solution methodology than the black-box methods that have been extensively used in the literature. This study takes the optimization-based method one step further and proposes a novel, multi-thread hybrid metaheuristic approach for fake news detection in social media. The most innovative feature of the proposed method is that it uses a supervisor thread mechanism, which simultaneously monitors and improves the performance and search patterns of metaheuristic algorithms running parallel. With the supervisor thread mechanism, it is possible to analyse different key attribute combinations in the search space. In addition, this study develops a software framework that allows this model to be implemented easily. It tests the performance of the proposed model on three different data sets, respectively containing news about Covid-19, the Syrian War, and daily politics. The proposed method is evaluated in comparison to the results of fifteen different well-known deep models and classification algorithms. Experimental results prove the success of the proposed model and that it can produce competitive results. [ABSTRACT FROM AUTHOR]

Energy efficiency and throughput are concerns for energy-harvesting cognitive radio networks. However, attaining the maximum level of both requires optimization of sensing duration, harvested energy, and transmission time. To obtain the optimal values of these multiple parameters and to maximize the average throughput and energy efficiency, a new hybrid technique for multi-objective optimization is proposed. This hybrid optimization algorithm incorporates a Shapley value and a game theoretic concept into metaheuristics. Here, particle swarm optimization grey wolf optimization (PSOGWO) is selected as the source for the advanced hybrid algorithm. The concept of the unbiased nature of wolves is also added to PSOGWO to make it more efficient. Multi-objective optimization is formulated by taking a deep look into combined spectrum sensing and energy harvesting in a cognitive radio network (CSSEH). The Pareto optimal solutions for the multi-objective optimization problem of energy efficiency and throughput can be obtained using PSOGWO by updating the velocity with the weights. In the proposed Shapley hybrid multi-objective optimization algorithm, we used Shapley values to set up the weights that, in turn, updated the velocities of the particles. This updated velocity increased the ability of particles to reach a global optimum rather than becoming trapped in local optima. The solution obtained with this hybrid algorithm is the Shapley–Pareto optimal solution. The proposed algorithm is also compared with state-of-the-art PSOGWO, unbiased PSOGWO, and GWO. The results show a significant level of improvement in terms of energy efficiency by 3.56% while reducing the sensing duration and increasing the average throughput by 21.83% in comparison with standard GWO. [ABSTRACT FROM AUTHOR]

International Diabetes Federation (IDF) reports that diabetes is a rapidly growing illness. About 463 million adults between 20-79 years have diabetes. There are also millions of undiagnosed patients. It is estimated that there will be about 578 million diabetics by 2030 [1]. Diabetes reasons different eye diseases. Diabetic retinopathy (DR) is one of them and is also one of the most common vision loss or blindness worldwide. DR progresses slowly and has few indicators in the early stages. It makes the diagnosis of DR a problematic task. Automated systems promise to support the diagnosis of DR. Many deep learning-based models have been developed for DR classification. This study aims to support ophthalmologists in the diagnosis process and increase the diagnosis performance of DR through a hybrid model. A publicly available Messidor-2 dataset was used in this study, comprised of retinal images. In the proposed model, images were preprocessed, and a deep learning model, namely, InceptionV3, was used in feature extraction, where a transfer learning approach is applied. Next, the number of features in obtained feature vectors was decreased with feature selection by Simulated Annealing. Lastly, the best representation features were used in the XGBoost model. The XGBoost algorithm gives an accuracy of 92.55% in a binary classification task. This study shows that a pre-trained ConvNet with a metaheuristic algorithm for feature selection gives a satisfactory result in the diagnosis of DR. [ABSTRACT FROM AUTHOR]

The Capacitated vehicle routing problem (CVRP) is a complex transportation issue that involves designing a set of routes for a group of homogenous vehicles to serve a set of customers at shortest distance travelled. It belongs to the class of NP-hard problems with a high computational complexity, and then it is difficult to solve this problem with traditional optimization methods when the problem size is large. Consequently, in this study, an improved swarm optimization metaheuristic approach has been proposed based on intelligent foraging behavior of honey bees with several specifically designed local search operators and features to solve this problem. The effectiveness of the proposed approach was illustrated on the standard benchmark problems. The results revealed that the proposed approach has fast convergence rate and high computational accuracy. Overall, our experiments show that our approach could be a very efficient approach for solving the CVRP and its experimental results are competitive in terms of the quality of the solutions reported in other well-known methods. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimization of steel trusses using direct analysis. The total weight and the inter-story drift or displacements of the structure were two conflict objectives, while the constraints relating to strength and serviceability load combinations were evaluated using nonlinear inelastic and nonlinear elastic analyses, respectively. Six common metaheuristic algorithms such as nondominated sorting genetic algorithm-II (NSGA-II), NSGA-III, generalized differential evolution (GDE3), PSO-based MOO using crowding, mutation, and ε-dominance (OMOPSO), improving the strength Pareto evolutionary algorithm (SPEA2), and multi-objective evolutionary algorithm based on decomposition (MOEA/D) were applied to solve the developed MOO problem. Four truss structures were studied including a planar 10-bar truss, a spatial 72-bar truss, a planar 47-bar powerline truss, and a planar 113-bar truss bridge. The numerical results showed a nonlinear relationship and inverse proportion between the two objectives. Furthermore, all six algorithms were efficient at finding feasible optimal solutions. No algorithm outperformed the others, but NSGA-II and MOEA/D seemed to be better at both searching Pareto and anchor points. MOEA/D was also more stable and yields a better solution spread. OMOPSO was also good at solution spread, but its stability was worse than MOEA/D. NSGA-III was less efficient at finding anchor points, although it can effectively search for Pareto points. [ABSTRACT FROM AUTHOR]

A new metaheuristic algorithm called green anaconda optimization (GAO) which imitates the natural behavior of green anacondas has been designed. The fundamental inspiration for GAO is the mechanism of recognizing the position of the female species by the male species during the mating season and the hunting strategy of green anacondas. GAO's mathematical modeling is presented based on the simulation of these two strategies of green anacondas in two phases of exploration and exploitation. The effectiveness of the proposed GAO approach in solving optimization problems is evaluated on twenty-nine objective functions from the CEC 2017 test suite and the CEC 2019 test suite. The efficiency of GAO in providing solutions for optimization problems is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that the proposed GAO approach has a high capability in exploration, exploitation, and creating a balance between them and performs better compared to competitor algorithms. In addition, the implementation of GAO on twenty-one optimization problems from the CEC 2011 test suite indicates the effective capability of the proposed approach in handling real-world applications. [ABSTRACT FROM AUTHOR]

This paper introduced a novel metaheuristic that is developed based on the tournament mechanism, namely quad tournament optimizer (QTO). As its name suggests, QTO proposes a new approach of metaheuristic in which there are four searches conducted by each agent in every iteration. These searches are: (1) searching toward the global best solution, (2) searching toward the middle between the global best solution and a randomly selected solution, (3) searching relative to a randomly selected solution, and (4) neighbourhood search around the corresponding solution and the global best solution. A solution candidate is generated by each search. Then, a tournament is carried out to find the best candidate. This strategy is novel because most of metaheuristic deploys only single search or multiple searches where each search is conducted sequentially. QTO is challenged to find the optimal solution of 23 classic functions. In this challenge, QTO is benchmarked against five shortcoming metaheuristics: marine predator algorithm (MPA), slime mould algorithm (SMA), golden search optimizer (GSO), hybrid pelican Komodo algorithm (HPKA), and guided pelican algorithm (GPA). The result indicates that QTO outperforms all these benchmark metaheuristics. QTO is better than MPA, SMA, GSO, HPKA, and GPA in solving 22, 19, 21, 20, and 13 functions consecutively. The result also indicates that QTO has successfully found the global optimal solution for nine functions: Sphere, Schwefel 2.22, Schwefel 1.2, Schwefel 2.21, Rastrigin, Griewank, Six Hump Camel, Branin, and Goldstein-Price. [ABSTRACT FROM AUTHOR]

Sustainable development requires improvements in the use of natural resources. The main objective of the present study was to optimize the use of materials in the construction of reinforced concrete precast hinged frames. Proprietary software was developed in the Python programming language. This allowed the structure's calculation, verification and optimization through the application of metaheuristic techniques. The final cost is a direct representation of the use of materials. Thus, three algorithms were applied to solve the economic optimization of the frame. By applying simulated annealing, threshold accepting and old bachelor's acceptance algorithms, sustainable, non-traditional designs were achieved. These make optimal use of natural resources while maintaining a highly restricted final cost. In order to evaluate the environmental impact improvement, the carbon-dioxide-associated emissions were studied and compared with a reference cast-in-place reinforced concrete frame. The results showed designs with reduced upper slab and lateral wall depth and dense passive reinforcement. These were able to reduce up to 24% of the final cost of the structure as well as over 30% of the associated emissions. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a recent population-based metaheuristic widely used for solving optimization problems. However, the emerging large-scale optimization problems pose a great challenge for AOA due to its prohibitive computational cost to traverse the huge solution space effectively. This article proposes a parallel Spark-AOA using Scala on Apache Spark computing platform. Spark-AOA leverages the intrinsic parallel nature of the population-based AOA and the native iterative in-memory computation support of Spark through resilient distributed datasets (RDD) to accelerate the optimization process. Spark-AOA divides the solutions population into several subpopulations that are distributed into multiple RDD partitions and manipulated concurrently. Simulation experiments on different benchmark functions with up to 1,000-dimension and three engineering design problems demonstrate that Spark-AOA outperforms considerably standard AOA and Spark-based implementations of two recent metaheuristics both in terms of run-time and solution quality. [ABSTRACT FROM AUTHOR]

This article introduces a new metaheuristic algorithm called the Serval Optimization Algorithm (SOA), which imitates the natural behavior of serval in nature. The fundamental inspiration of SOA is the serval's hunting strategy, which attacks the selected prey and then hunts the prey in a chasing process. The steps of SOA implementation in two phases of exploration and exploitation are mathematically modeled. The capability of SOA in solving optimization problems is challenged in the optimization of thirty-nine standard benchmark functions from the CEC 2017 test suite and CEC 2019 test suite. The proposed SOA approach is compared with the performance of twelve well-known metaheuristic algorithms to evaluate further. The optimization results show that the proposed SOA approach, due to the appropriate balancing exploration and exploitation, is provided better solutions for most of the mentioned benchmark functions and has superior performance compared to competing algorithms. SOA implementation on the CEC 2011 test suite and four engineering design challenges shows the high efficiency of the proposed approach in handling real-world optimization applications. [ABSTRACT FROM AUTHOR]

This paper demonstrates a novel simple metaheuristic algorithm, the cone search (CS). This name comes from the distinct strategy of CS in searching the suboptimal solution. In the early iteration, its local search space is wide to facilitate exploration. The local search space is reduced linearly during the iteration so that the exploration changes to exploitation gradually. As a swarm intelligence, CS contains several autonomous agents and a collective intelligence called memory. This memory consists of several best solutions. In this work, CS is challenged to find the global optimal of 23 benchmark functions. In the simulation, CS is compared with four metaheuristic algorithms: particle swarm optimization (PSO), marine-predators algorithm (MPA), Komodo mlipir algorithm (KMA), and pelican optimization algorithm (POA). The result shows that CS performs well in solving these 23 functions. Moreover, it can find the global optimal of six fixeddimension multimodal functions: Branin, six hump camel, Hartman 6, Shekel 5, Shekel 7, and Goldstein price. CS beats PSO, MPA, KMA, and POA in solving 23, 22, 21, 22 functions respectively. [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]

Metaheuristic algorithms (MAs) are used to find the answers to NP-Hard problems. NP-Hard problems basically refer to a set of optimization problems that cannot be solved in a polynomial at a time. MAs try to find the optimal or near-definitive answer in the shortest possible time to solve such problems and a set of optimization algorithms with different origins. These algorithms may be inspired by the natural sciences, physics, mathematics, and political science. However, a particular Metaheuristic algorithm may not provide the best answer to all problems. Each MA may have a better response to specific problems than other similar algorithms. Therefore, researchers will try to introduce and discover new algorithms to find optimal answers to a wide range of problems. In this paper, a new Meta-heuristic algorithm called the Water optimization algorithm (WAO) is presented. WAO is inspired by the chemical and physical properties of water molecules. The main idea of the proposed algorithm is to link water molecules together to find the optimal points. Factors such as particle motion, particle evaporation, and particle bonding have created a mechanism based on swarm intelligence and physical intelligence that inspired this algorithm to solve persistent problems. In this algorithm, answers are defined as a water molecule, a set of them is defined as a local answer. Water bonds provide the right move towards the optimal response. In evaluating the performance of the proposed algorithm, the proposed method is applied to some standard functions and some practical problems. The results obtained from the experiments show that the proposed algorithm has provided appropriate and acceptable answers in terms of execution time and accuracy compared to some similar algorithms. [ABSTRACT FROM AUTHOR]

Since the increases in electricity demand, environmental awareness, and power reliability requirements, solutions of single-objective optimal power flow (OPF) and multi-objective OPF (MOOPF) (two or three objectives) problems are inadequate for modern power system management and operation. Solutions to the many-objective OPF (more than three objectives) problems are necessary to meet modern power-system requirements, and an efficient optimization algorithm is needed to solve the problems. This paper presents a many-objective marine predators algorithm (MaMPA) for solving single-objective OPF (SOOPF), multi-objective OPF (MOOPF), and many-objective OPF (MaOPF) problems as this algorithm has been widely used to solve other different problems with many successes, except for MaOPF problems. The marine predators algorithm (MPA) itself cannot solve multi- or many-objective optimization problems, so the non-dominated sorting, crowding mechanism, and leader mechanism are applied to the MPA in this work. The considered objective functions include cost, emission, transmission loss, and voltage stability index (VSI), and the IEEE 30- and 118-bus systems are tested to evaluate the algorithm performance. The results of the SOOPF problem provided by MaMPA are found to be better than various algorithms in the literature where the provided cost of MaMPA is more than that of the compared algorithms for more than 1000 USD/h in the IEEE 118-bus system. The statistical results of MaMPA are investigated and express very high consistency with a very low standard deviation. The Pareto fronts and best-compromised solutions generated by MaMPA for MOOPF and MaOPF problems are compared with various algorithms based on the hypervolume indicator and show superiority over the compared algorithms, especially in the large system. The best-compromised solution of MaMPA for the MaOPF problem is found to be greater than the compared algorithms around 4.30 to 85.23% for the considered objectives in the IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

Moth search (MS) is a nature-inspired metaheuristic optimization algorithm based on the most representative characteristics of moths, Lévy flights and phototaxis. Phototaxis signifies a movement which organism towards or away from a source of light, which is the representative features for moths. The best moth individual is seen as the light source in Moth search. The moths that have a smaller distance from the best one will fly around the best individual by Lévy flights. For reasons of phototaxis, the moths, far from the fittest one, will fly towards the best one with a big step. These two features, Lévy flights and phototaxis, correspond to the processes of exploitation and exploration for metaheuristic optimization. The superiority of the moth search has been demonstrated in many benchmark problems and various application areas. A comprehensive survey of the moth search was conducted in this paper, which included the three sections: statistical research studies about moth search, different variants of moth search, and engineering optimization/applications. The future insights and development direction in the area of moth search are also discussed. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems. For optimization problems, metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions. Most of the existing metaheuristic algorithms are designed for serial systems. Meanwhile, existing algorithms still have a lot of room for improvement in convergence speed, robustness, and performance. To address these issues, this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization (TCCO) inspired by the process of human team cooperation and competition. The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions. In order to evaluate the performance of the proposed algorithm, this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm (GOA), Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA) and Sparrow Search Algorithm (SSA). Experiment results of 30 test functions commonly used in the optimization field indicate that, compared with these current advanced metaheuristic algorithms, TCCO has strong competitiveness in both solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

This paper introduces a novel physical-inspired metaheuristic algorithm called "Light Spectrum Optimizer (LSO)" for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm. [ABSTRACT FROM AUTHOR]

The current variety of the Internet Protocol is IPv6 for addressing the networking devices as per the mechanisms proposed by past researchers to minimize the delay and improve the efficiency. It is additionally recognized as a classless addressing scheme that locates computing machines throughout the web so they can be located. Among the mechanisms, the Group Intelligence (GI) algorithms that consists of Evolutionary and Group based Optimization techniques have gained attention of the researchers to contribute an effective optimized solution towards solving an optimization problem. Thus, the aim of this paper is to study the implementation, features and effectiveness of different GI based metaheuristic machine learning optimization algorithms, so as to contribute in future towards designing and upgrade to a new IPv6 addressing scheme by blending the benefits of the metaheuristic algorithms to find good or near --optimal solutions at a reasonable computation cost in IPv6 network to enhance the execution result of addressing scheme on real time data. [ABSTRACT FROM AUTHOR]

The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm's primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO's robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively. [ABSTRACT FROM AUTHOR]

In today’s data-centric world, the significance of generated data has increased manifold. Clustering the data into a similar group is one of the dynamic research areas among other data practices. Several algorithms’ proposals exist for clustering. Apart from the traditional algorithms, researchers worldwide have successfully employed some metaheuristic approaches for clustering. The crow search algorithm (CSA) is a recently introduced swarm-based algorithm that imitates the performance of the crow. An effective crow search algorithm (ECSA) has been proposed in the present work, which dynamically attunes its parameter to sustain the search balance and perform an oppositional-based random initialization. The ECSA is evaluated over CEC2019 Benchmark Functions and simulated for data clustering tasks compared with well-known metaheuristic approaches and famous partition-based K-means algorithm over benchmark datasets. The results reveal that the ECSA performs better than other algorithms in the context of external cluster quality metrics and convergence rate. [ABSTRACT FROM AUTHOR]

The current study investigates the Intelligent Water Drops (IWD) metaheuristic algorithm to construct and produce good quality solutions for the university examination timetabling problem (UETP). The IWD is a population-based metaheuristic that simulates the dynamic of the river systems. The main motivations for investigating IWD algorithm for examination timetabling problem is the ability to explore the search space effectively. The main drawback of IWD algorithm is like other population-based algorithm in exploitation process where it is very efficient scanning several search space niches, but it is unable to drilling down in each niche to which it navigates. In this paper we propose a hybrid approach based on IWD and locale search algorithm to improve the exploitation of IWD algorithm. The experimental results demonstrated that the proposed algorithm (i.e., Hybrid IWD) obtained best results in three datasets when comparing with the best-known results performed by the swarm intelligent approaches. Finally, the proposed algorithm achieved one best results in comparison with the other metaheuristic approaches. [ABSTRACT FROM AUTHOR]

This paper presents the discrete version of the Mycorrhiza Tree Optimization Algorithm (MTOA), using the Lotka–Volterra Discrete Equation System (LVDES) formed by the Predator–Prey, Cooperative and Competitive Models. The Discrete Mycorrhizal Optimization Algorithm (DMOA) is a stochastic metaheuristic that integrates randomness in its search processes. These algorithms are inspired by nature, specifically by the symbiosis between plant roots and a fungal network called the Mycorrhizal Network (MN). The communication in the network is performed using chemical signals of environmental conditions and hazards, the exchange of resources, such as Carbon Dioxide (CO2) that plants perform through photosynthesis to the MN and to other seedlings or growing plants. The MN provides water (H2O) and nutrients to plants that may or may not be of the same species; therefore, the colonization of plants in arid lands would not have been possible without the MN. In this work, we performed a comparison with the CEC-2013 mathematical functions between MTOA and DMOA by conducting Hypothesis Tests to obtain the efficiency and performance of the algorithms, but in future research we will also propose optimization experiments in Neural Networks and Fuzzy Systems to verify with which methods these algorithms perform better. [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]

The exploration and exploitation (E&E) of a search space are fundamental processes in many fields of artificial intelligence. Indeed, it is important to ensure that many regions of the search space are examined, so as not to get trapped in a local optimum, but also that the promising regions are examined more in depth in order to find good local optima. Metaheuristics (MHs) are popular to perform E&E tasks. Explaining the dynamics of both processes is a repeated demand of researchers and practitioners, but remains a challenge. This paper presents an approach giving information about how a MH explores and exploits the search space. This approach is based on a way of representing transitions through presence variation vectors and associated proposed indicators, used to make transitions between E&E explicit and intelligible. Experimentations show the relevance of the proposed indicators. [ABSTRACT FROM AUTHOR]

In this article, we are presenting a new nature-inspired metaheuristic optimization algorithm that we call the Mycorrhiza Tree Optimization Algorithm (MTOA) focused on solving optimization problems. The algorithm is inspired by the relationship between trees and the Mycorrhiza Network (MN). In this relationship, there are interactions such as defense, communication, resource exchange and habitat colonization between the Trees and the MN. The Lotka–Volterra continuous systems (Predator–Prey, Cooperative and Competitive Models) were used for the design of the algorithm. To verify the efficiency of the algorithm, experiments with 36 mathematical functions were performed, but we are only presenting results of the functions with which comparisons were made with other methods. Hypothesis tests were also carried out, where in most of the results the MTOA algorithm was better by 70% and only in the experiments in 100 dimensions it was better by 60%. We believe that the algorithm has the potential to solve problems in control and optimization of neural networks, for which experimentation will be done as future work. [ABSTRACT FROM AUTHOR]

The vehicle routing problem (VRP) is one of the challenging problems in optimization and can be described as combinatorial optimization and NP-hard problem. Researchers have used many artificial intelligence techniques in order to try to solve this problem. Among these techniques, metaheuristic algorithms that can perform random search are the most promising because they can be used to find the right solution in the shortest possible time. Therefore, in this paper, the Harris hawks optimization (HHO) algorithm was used to attempt to solve the VRP. The algorithm was applied to 10 scenarios and the experimental results revealed that the HHO had a strong ability to check for and find the best route as compared to other metaheuristic algorithms, namely, simulated annealing and artificial bee colony optimization. The comparison was based on three criteria: minimum objective function obtained, minimum number of iterations required and satisfaction of capacity constraints. In all scenarios, the HHO showed clear superiority over the other methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • Metaheuristic method was employed in the field of plastic waste gasification. • Multi-objective particle swarm optimization was used for plastic gasification. • Pareto-front solutions obtained from MOPSO were ranked using GRA. • Lower heating value of 517 kJ/kg was obtained for polypropylene gasification. • Optimized cold gas efficiency and exergy efficiency of 84% and 57% were achieved. In recent decades, there has been a surge in demand for the development of renewable energies, leading to extensive research efforts focused on the gasification process. Consequently, a wide range of studies has been conducted to explore different feedstocks for gasification, with particular emphasis on plastic waste. Although a wide range of studies conducted various optimization methods for the gasification process, there was no specific study to employ metaheuristic methods in the field of plastic waste gasification. This study aimed to perform a multi-objective particle swarm optimization (MOPSO) method to solve a multi-objective plastic waste gasification optimization problem for enhancing energetic and exergetic viewpoints. Moreover, exact methods including non-linear optimization and response surface methodology (RSM) were conducted to be compared with the results derived from MOPSO. Pareto-front solutions obtained from MOPSO were ranked using grey relational analysis (GRA) and the optimum responses were 517 kJ/kg, 84 %, and 57 % for lower-heating value, cold gas efficiency, and exergy efficiency, respectively, which were compared to optimum options obtained from multi-objective non-linear optimization and RSM techniques. While the MOPSO model may not have performed as well as other methods in this particular problem, it is important to acknowledge its capabilities. Future research can concentrate on improving the behavior and performance of the MOPSO method when dealing with a larger number of points or when applied to different problem domains. [ABSTRACT FROM AUTHOR]

The proposed metaheuristic optimization algorithm based on the two-step Adams-Bashforth scheme (MOABT) was used in this paper for Multilayer Perceptron Training (MLP). In computer science and mathematical examples, metaheuristic is high-level procedures or guidelines designed to find, devise, or select algorithmic research methods to obtain high-quality solutions to an example problem, especially if the information is insufficient or incomplete, or if computational capacity is limited. Many metaheuristic methods include some stochastic example operations, which means that the resulting solution is dependent on the random variables that are generated during the search. The use of higher evidence can frequently find good solutions with less computational effort than iterative methods and algorithms because it searches a broad range of feasible solutions at the same time. Therefore, metaheuristic is a useful approach to solving example problems. There are several characteristics that distinguish metaheuristic strategies for the research process. The goal is to efficiently explore the search perimeter to find the best and closest solution. The techniques that make up metaheuristic algorithms range from simple searches to complex learning processes. Eight model data sets are used to calculate the proposed approach, and there are five classification data sets and three proximate job data sets included in this set. The numerical results were compared with those of the well-known evolutionary trainer Gray Wolf Optimizer (GWO). The statistical study revealed that the MOABT algorithm can outperform other algorithms in terms of avoiding local optimum and speed of convergence to global optimum. The results also show that the proposed problems can be classified and approximated with high accuracy. [ABSTRACT FROM AUTHOR]

Determining the topology, layout, and size of structural elements is one of the most important aspects in designing steel seismic-resistant structures. Optimization of these parameters is beneficial to find the lightest weight of the structure, thus reducing construction cost. This study compares the performance of three metaheuristic algorithms, namely, Particle Swarm Optimization (PSO), Symbiotic Organisms Search (SOS), and Differential Evolution (DE). Three study cases are used in order to find the lightest structural weight without violating constraints based on SNI 1726:2019, SNI 1729:2020, and SNI 7860:2020. The results of this study show that SOS has better performance than other algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an approach to overcome economic load dispatch (ELD) using a metaheuristic algorithm. Economic load dispatch (ELD) is one of the most important problems in a power system, and solving it quickly is extremely important. The main problem that will be addressed in this paper is how to optimize the economy of the power grid with various operational limitations, the loss in transmission line power, and consider minimizing the fuel costs produced. In this study, some of the newest metaheuristics inspired by nature will be explored, namely Seagull Optimization Algorithm (SOA), Marine Predator Algorithm (MPA), Sine Tree-Seed Algorithm (STSA), Chimp Optimization Algorithm (ChOA), Equilibrium Optimizer (EO), and Giza Pyramids Construction (GPC). The performance appraisal of the method applied in this study was tested using 2 case studies, namely a system with 3 and 6 power system units. The results are presented by comparing between metaheuristic and mathematical methods. The experimental results is showed that the Sine Tree-Seed Algorithm (STSA) is presented the best performance with various case studies with constraints. [ABSTRACT FROM AUTHOR]

خؽارزمية عذيخة الدخقاط )MCA )ىي خؽارزمية )metaheuristic )قائمة عمى الطبيعة والتي تحاكي عمى خخيطة ً ا عمى خؽارزمية عذيخة ميخكات بناء ً الدمؽك الحكي لحيؽان الدخقاط. تقجم ىحه الؽرقة تحدين فؽضؽية واستخاتيجية التقاطع )CC-MCA .)تعمل ىاتان االستخاتيجيتان عمى زيادة تنؽيع وتكثيف الخؽارزمية المقتخحة وتعديد القجرة عمى البحث إليجاد المديج مؼ الحمؽل عالية الجؽدة. تػ حل مذكمة حقيبة العيخ 0-1 بؽاسطة MCA األساسي والندخة المحدنة مؼ ىحه نة ّ الخؽارزمية المحد CC-MCA .تػ اختبار أداء ىحه الخؽارزميات عمى مدائل ذات أبعاد منخفضة وعالية. أظيخت النتائج التجخيبية أن الخؽارزمية المقتخحة تغمبت عمى الخؽارزمية األساسية مؼ حيث جؽدة الحل وسخعتو واكتدبت األمثمية مع مذاكل األبعاد المنخفضة. عالوة عمى ذلغ ، في مذاكل األبعاد العالية ، ليا نتائج تنافدية مقارنة بالخؽارزميات األخخى. [ABSTRACT FROM AUTHOR]

Harris hawks optimization (HHO) is a population-based metaheuristic algorithm, inspired by the hunting strategy and cooperative behavior of Harris hawks. In this study, HHO is hybridized with 10 different chaotic maps to adjust its critical parameters. Hybridization is performed using four different methods. First, 15 test functions with unimodal and multimodal features are used for the analysis to determine the most successful chaotic map and the hybridization method. The results obtained reveal that chaotic maps increase the performance of HHO and show that the piecewise map method is the most effective one. Moreover, the proposed chaotic HHO is compared to four metaheuristic algorithms in the literature using the CEC2019 set. Next, the proposed chaotic HHO is applied to three mechanical design problems, including pressure vessel, tension/compression spring, and three-bar truss system as benchmarks. The performances and results are compared with other popular algorithms in the literature. They show that the proposed chaotic HHO algorithm can compete with HHO and other algorithms on solving the given engineering problems very successfully. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the authors aim to examine and comparatively evaluate a recently-developed metaheuristic called crystal structure algorithm (CryStAl) – which is inspired by the symmetries in the internal structure of crystalline solids – in solving engineering mechanics and design problems. Design/methodology/approach: A total number of 20 benchmark mathematical functions are employed as test functions to evaluate the overall performance of the proposed method in handling various functions. Moreover, different classical and modern metaheuristic algorithms are selected from the optimization literature for a comparative evaluation of the performance of the proposed approach. Furthermore, five well-known mechanical design examples are utilized to examine the capability of the proposed method in dealing with challenging optimization problems. Findings: The results of this study indicated that, in most cases, CryStAl produced more accurate outputs when compared to the other metaheuristics examined as competitors. Research limitations/implications: This paper can provide motivation and justification for the application of CryStAl to solve more complex problems in engineering design and mechanics, as well as in other branches of engineering. Originality/value: CryStAl is one of the newest metaheuristic algorithms, the mathematical details of which were recently introduced and published. This is the first time that this algorithm is applied to solving engineering mechanics and design problems. [ABSTRACT FROM AUTHOR]

Scheduling is a decision-making process that plays an important role in the service and production industries. Effective scheduling can assist companies to survive in the competitive market. Single machine scheduling is an important optimization problem in the scheduling research area. It can be found in a wide range of real-world engineering problems, from manufacturing to computer science. Due to the high complexity of single machine scheduling problems, developing approximation methods, particularly metaheuristic algorithms, for solving them have absorbed considerable attention. In this study, a Lion Optimization Algorithm (LOA) is employed to solve a single machine with maintenance activities, where the objective is to minimize the Total Absolute Deviation of Compilation Times (TADC). In the scheduling literature, TADC as an objective function has hardly been studied. To evaluate the performance of the LOA, it was compared against a set of well-known metaheuristics. Therefore, a set of problem was generated, and a comprehensive experimental analysis was conducted. The results of computational experiments indicate the superiority of the proposed optimization method. [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]

Neutrosophic set is a branch of neutrosophy concerned with nature, the genesis, and breadth of impartialities, and also their interaction with various mental spectra. Neutrosophic sets constitute relatively new expansions of intuitionistic fuzzy. In a short period of time, numerous researchers have accepted neutrosophic reasoning. Many researchers have linked neutrosophic science and metaheuristics in various ways over the last ten years. Metaheuristic research has attracted a great attention throughout the literature, which covers methodologies, apps, comparative analysis; due to its higher intensities and fruitful implementations. Metaheuristic algorithms are used to introduce the best or the optimum solutions to a lot of optimization problems due to the behavior of these algorithms inspired by Nature and its ability to adapt to problems, as well as the possibility of integrating more than one algorithm to reach the best solutions. Based on the previous reason, many researchers used these algorithms with neutrosophic science to present many platforms in the recent years, which was the motivation to introduce this survey paper. This paper is introduced to cover the publications from 2010 to 2021 in order to draw a comprehensive picture of metaheuristic research integrated with neutrosophic theory. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is considered as one of the core concepts in the areas of machine learning and data mining which immensely impacts the performance of classification model. Through FS, irrelevant or partially relevant features can be eliminated which in turn helps in enhancing the performance of the model. Over the years, researchers have applied different meta-heuristic optimization techniques for the purpose of FS as these overcome the limitations of traditional optimization approaches. Going by the trend, we introduce a new FS approach based on a recently proposed meta-heuristic algorithm called Manta ray foraging optimization (MRFO) which is developed following the food foraging nature of the Manta rays, one of the largest known marine creatures. As MRFO is apposite for continuous search space problems, we have adapted a binary version of MRFO to fit it into the problem of FS by applying eight different transfer functions belonging to two different families: S-shaped and V-shaped. We have evaluated the eight binary versions of MRFO on 18 standard UCI datasets. Of these, the best one is considered for comparison with 16 recently proposed meta-heuristic FS approaches. The results show that MRFO outperforms the state-of-the-art methods in terms of both classification accuracy and number of features selected. The source code of this work is available in https://github.com/Rangerix/MetaheuristicOptimization. [ABSTRACT FROM AUTHOR]

In recent decades, researches on optimizing the parameter of the artificial neural network (ANN) model has attracted significant attention from researchers. Hybridization of superior algorithms helps improving optimization performance and capable of solving complex applications. As a traditional gradient-based learning algorithm, ANN suffers from a slow learning rate and is easily trapped in local minima when training techniques such as gradient descent (GD) and back-propagation (BP) algorithm are used. The characteristics of randomization and selection of the best or near-optimal solution of metaheuristic algorithm provide an effective and robust solution; therefore, it has always been used in training of ANN to improve and overcome the above problems. New metaheuristic algorithms are proposed every year. Therefore, the review of its latest developments is essential. This article attempts to summarize the metaheuristic algorithms which have been proposed from the year 1975 to 2020 from various journals, conferences, technical papers, and books. The comparison of the popularity of the metaheuristic algorithm is presented in two time frames, such as algorithms proposed in the recent 20 years and those proposed earlier. Then, some of the popular metaheuristic algorithms and their working principle are reviewed. This article further categorizes the latest metaheuristic search algorithm in the literature to indicate their efficiency in training ANN for various industry applications. More and more researchers tend to develop new hybrid optimization tools by combining two or more metaheuristic algorithms to optimize the training parameters of ANN. Generally, the algorithm's optimal performance must be able to achieve a fine balance of their exploration and exploitation characteristics. Hence, this article tries to compare and summarize the properties of various metaheuristic algorithms in terms of their convergence rate and the ability to avoid the local minima. This information is useful for researchers working on algorithm hybridization by providing a good understanding of the convergence rate and the ability to find a global optimum. [ABSTRACT FROM AUTHOR]

This paper proposes a novel metaheuristic-based optimization technique called search space reduction (SSR) optimization algorithm. This algorithm attempts to solve the common pitfalls in the existing algorithms in the literature by randomly generating the search agents in every iteration instead of following the best solution. This new algorithm is simple, computationally efficient, which is based on the concept of reducing the search space. The performance of this algorithm is tested over classical test functions and CEC'17 benchmark test functions. The results are compared with well-established algorithms in the literature. The test results show that the proposed algorithm exhibits good exploration and exploitation capabilities. Further, this algorithm also outperforms other algorithms in solving multimodal optimization problems. In addition to this, the computational complexity of this algorithm is also presented according to CEC'17 guidelines. The proposed algorithm is also employed to solve three engineering design problems and a more complex re-entry trajectory optimization problem to show its effectiveness. [ABSTRACT FROM AUTHOR]