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This paper presents a novel bio-inspired metaheuristic algorithm termed the Fossa Optimization Algorithm (FOA), which emulates the natural hunting behavior of the fossa in its habitat. FOA draws its core inspiration from the two-stage hunting technique of the fossa, involving an initial attack on a spotted lemur followed by a pursuit through the trees. The theoretical framework of FOA is elaborated, and its implementation is mathematically modeled in two distinct phases: (i) exploration, which simulates the fossa's positional adjustments during the initial attack on the lemur, and (ii) exploitation, which models the positional changes of the fossa during the chase. The efficacy of FOA is tested against twenty-two constrained optimization problems from the CEC 2011 test suite, as well as four engineering design challenges. The optimization results demonstrate FOA's strong capabilities in both exploration and exploitation, maintaining a balance that facilitates convergence to optimal solutions. FOA's performance is benchmarked against twelve established algorithms, showing that it consistently outperforms its competitors by delivering superior results and ranking as the top optimizer in most of the evaluated functions. These findings indicate that FOA is highly effective in addressing optimization tasks in real-world scenarios. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) problem is a popular optimization problem in electrical engineering. Many studies that handled ED problem employed metaheuristics as the optimization technique. Unfortunately, these metaheuristics are old or the existing ones. Meanwhile, ED problem is not the favourite constrained case in studies that introduced a novel metaheuristic. This research introduces a novel metaheuristic called quadratic time optimization (QTO). QTO comprises three sequential searches where both diversification and intensification are blended in every search. But the portion of both orientations changes by following the quadratic time. The appraisal of QTO is performed by employing it to solve both unconstrained and constrained problems. The 23 traditional functions represent the unconstrained problem while economic load dispatch (ELD) problem with valve point effect and spinning reserve problem represents the constrained problem. In both appraisals, there are five novel metaheuristics represent the benchmarks, including: walrus optimization algorithm (WaOA), coati optimization algorithm (COA), total interaction algorithm (TIA), addax optimization algorithm (AOA), and language education optimization (LEO). The result shows the competitiveness of the proposed QTO in handling both unconstrained and constrained problems. QTO becomes the first best in 8 functions out of 23 functions including five high dimension unimodal functions, two high dimension multimodal functions, and one fixed dimension multimodal functions. The result also shows that QTO is competitive in three use cases in ELD problems as the ratio between the range and average total cost among metaheuristics for the first, second, and third cases is 0.72, 0.04, and 0.02 percent consecutively. [ABSTRACT FROM AUTHOR]

Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat's position change while moving towards the prey and (ii) exploitation based on simulating the bobcat's position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel optimization algorithm called the OX optimizer, inspired by oxen animals, which are characterized by their great strength. The OX optimizer is designed to address the challenges posed by complex, high-dimensional optimization problems. The design of the OX optimizer embodies a fundamental symmetry between global and local search processes. This symmetry ensures a balanced and effective exploration of the solution space, highlighting the algorithm's innovative contribution to the field of optimization. The OX optimizer has been evaluated on CEC2022 and CEC2017 IEEE competition benchmark functions. The results demonstrate the OX optimizer's superior performance in terms of convergence speed and solution quality compared to existing state-of-the-art algorithms. The algorithm's robustness and adaptability to various problem landscapes highlight its potential as a powerful tool for solving diverse optimization tasks. Detailed analysis of convergence curves, search history distributions, and sensitivity heatmaps further support these findings. Furthermore, the OX optimizer has been applied to optimize support vector machines (SVMs), emphasizing parameter selection and feature optimization. We tested it on the NSL-KDD dataset to evaluate its efficacy in an intrusion detection system. The results demonstrate that the OX optimizer significantly enhances SVM performance, facilitating effective exploration of the parameter space. [ABSTRACT FROM AUTHOR]

This article introduces a novel stochastic optimization method termed the Best-Other Algorithm (BOA). The nomenclature reflects its reliance on the best member, which is amalgamated with other entities. BOA, a metaphor-free swarm-based metaheuristic, comprises three directed searches. The first involves subtracting the best member from a randomly selected member. The second entails determining the midpoint between the best member, and another randomly chosen member. The third centers around the midpoint between the best member and a random solution along the space. The efficacy of BOA is evaluated by challenging it to solve a collection of 23 functions. In this evaluation, BOA is pitted against five other metaheuristics: Northern Goshawk Optimization (NGO), Zebra Optimization Algorithm (ZOA), Coati Optimization Algorithm (COA), Migration Algorithm (MA), and Osprey Optimization Algorithm (OOA). The findings indicate the superiority of BOA over its counterparts. BOA outperforms NGO, ZOA, COA, MA, and OOA in 21, 15, 16, 15, and 17 functions, respectively. These results underscore the pivotal role of the best member as a reference and the comparatively lesser significance of the neighborhood search as the search space diminishes during the iteration. [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]

Image contrast is an important factor in distinguishing objects in the image from their background. Low-contrast images, caused by various factors such as poor lighting, are insufficient for human visual perception and many image processing applications. Therefore, image contrast enhancement (ICE) is a necessary preprocessing step in different image processing applications. The main purpose of ICE is to make image objects more easily distinguishable and to improve the quality of visual information in the image. In this paper, image contrast enhancement is studied as an optimization problem. First, a modified version of the gray wolf optimization (GWO) algorithm, a population-based meta-heuristic that mimics the social leadership and hunting behavior of gray wolves in nature, is adapted to the ICE problem. Second, a novel variant of GWO is proposed using Halton low-discrepancy sequence in the population initialization phase, instead of starting completely randomly. Third, unlike previous studies, an effective metric is used as a fitness function to measure the image quality by focusing on the contrast change without using a reference image. The experimental results on the TID2013 and CSIQ datasets show that the proposed Halton sequence-initialized GWO outperforms other variant metaheuristic algorithms and traditional histogram equalization-based methods, according to all utilized various evaluation metrics. [ABSTRACT FROM AUTHOR]

There are two problems in the development of swarm-based metaheuristic. First, there are not any metaheuristic is able to solve all problems superiorly. Second, the cross-dimension mapping between the entity and its reference during performing the directed search is rare to find. Based on these problems, this work introduces a new metaphorfree swarm-based metaheuristic called the triple-shake algorithm (TSA). As its name suggests, TSA consists of three directed searches. The reference in the first search is the balance mixture between the finest entity and the member of permutation set. The reference in the second search is the balance mixture between the finest entity and a randomly chosen entity. The reference in the third search is the finest entity only. But the cross-dimension mapping is performed in this third search with 50 percent probability. In the benchmark assessment, TSA is compared with zebra optimization algorithm (ZOA), walrus optimization algorithm (WaOA), migration algorithm (MA), total interaction algorithm (TIA), and one-toone based optimization (OOBO). The result indicates the dominance of TSA among its comparators. TSA is better than ZOA, WaOA, MA, TIA, and OOBO in 21, 19, 17, 19, and 22 functions consecutively out of 23 functions. [ABSTRACT FROM AUTHOR]

The no-free-lunch (NFL) theory has become the main reason of developing new metaheuristics in decades. Besides, the strict dimension mapping has been implemented in many population-based metaheuristics, especially the swarm-based metaheuristics. Regarding this problem, this paper proposes a new swarm-based metaheuristics called focus and shake algorithm (FSA). FSA has novel approach in the dimension mapping between the agent and its reference during the directed motion. It combines the strict dimension mapping called as focus approach and the randomized dimension mapping called as shake approach to enhance its exploration ability. FSA deploys two directed motions based on two references. The first reference is constructed based on the balance mixture between two finer agents while the second reference is constructed based on the balance mixture of the finest agent and a randomly picked agent. In the competing assessment, FSA competes with five brand new swarm-based metaheuristics: migration algorithm (MA), total interaction algorithm (TIA), lyrebird optimization algorithm (LOA), osprey optimization algorithm (OOA), and kookaburra optimization algorithm (KOA). The result exhibits that FSA is finer than MA, TIA, LOA, OOA, and KOA in 19, 21, 21, 19, and 20 functions out of 23 functions respectively. The result also shows that the superiority of FSA takes place in both unimodal and multimodal problems. In the future, the cross-dimension mapping can be more explored to develop finer swarm-based metaheuristics. [ABSTRACT FROM AUTHOR]

This paper introduces a novel metaheuristic named the stochastic shaking algorithm (SSA), which is rooted in swarm intelligence principles. The innovation lies in its unique utilization of iteration for selecting references during guided searches through a stochastic approach. The optimization process involves two sequential steps: the primary reference in the first step is the finest swarm member, while in the second step, it is the mean of all finer swarm members plus the finest one. This primary reference is then combined with a randomly chosen solution within the space, serving as the secondary reference. SSA undergoes evaluation in two contexts. The first involves assessing its performance using a set of 23 classic functions as a theoretical use case. The second involves tackling the economic load dispatch problem (ELD), a practical use case featuring a system with 13 generators of various energy resources. The study compares SSA against five other metaheuristics--One to One Based Optimization (OOBO), Kookaburra Optimization Algorithm (KOA), Language Education Optimization (LEO), Total Interaction Algorithm (TIA), and Walrus Optimization Algorithm (WaOA). Results indicate SSA's superiority over OOBO, KOA, LEO, TIA, and WaOA in 21, 13, 11, 16, and 14 functions out of 23 functions, respectively. Additionally, the evaluation of the economic load dispatch problem reveals intense competition among the six metaheuristics. [ABSTRACT FROM AUTHOR]

Supply Chain (SC) Optimization is a key activity in today's industry with the goal of increasing operational efficiency, reducing costs, and improving customer satisfaction. Traditional optimization methods often struggle to effectively use resources while handling complex and dynamic Supply chain networks. This paper introduces a novel biomimetic metaheuristic algorithm called the Wombat Optimization Algorithm (WOA) for supply chain optimization. This algorithm replicates the natural behaviors observed in wombats living in the wild, particularly focusing on their foraging tactics and evasive maneuvers towards predators. The theory of WOA is described and then mathematically modeled in two phases: (i) exploration based on the simulation of wombat movements during foraging and trying to find food and (ii) exploitation based on simulating wombat movements when diving towards nearby tunnels to defend against its predators. The effectiveness of WOA in addressing optimization challenges is assessed by handling the CEC 2017 test suite across various problem dimensions, including 10, 30, 50, and 100. The findings of the optimization indicate that WOA demonstrates a strong ability to effectively manage exploration and exploitation, and maintains a balance between them throughout the search phase to deliver optimal solutions for optimization problems. A total of twelve well-known metaheuristic algorithms are called upon to test their performance against WOA in the optimization process. The outcomes of the simulations reveal that WOA outperforms the other algorithms, achieving superior results across most benchmark functions and securing the top ranking as the most efficient optimizer. Using a Wilcoxon rank sum test statistical analysis, it has been proven that WOA outperforms other algorithms significantly. WOA is put to the test with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems to showcase its ability to solve real-world optimization problems. The results of the simulations demonstrate that WOA excels in real-world applications by delivering superior solutions and outperforming its competitors. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions. [ABSTRACT FROM AUTHOR]

This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, the BOA is formulated and mathematically modeled. Evaluation on the CEC 2017 test suite showcases the BOA's ability to balance exploration and exploitation, delivering competitive solutions. Comparative analysis against twelve well-known metaheuristic algorithms demonstrates the BOA's superior performance across various benchmark functions, with statistically significant advantages. Moreover, application to constrained optimization problems from the CEC 2011 test suite highlights the BOA's effectiveness in real-world optimization tasks. [ABSTRACT FROM AUTHOR]

Many recent swarm-based metaheuristics are trapped in the exploitation of the highest quality as the main or the only reference and the neighbourhood search with the reduction of local search space during the iteration. Regarding to this issue, this paper introduces a novel metaheuristic called swarm space hopping algorithm (SSHA). SSHA consists of three searches. First, a directed search toward the highest quality is performed. Second, the directed search toward the resultant of better agents or away from the other agent is performed. Third, the arithmetic crossover between the agent and a randomized solution in the first half or second half of space is performed. In this work, three evaluations are performed to assess the performance of SSHA. The first evaluation is the benchmark evaluation to compare the performance of SSHA with other recent metaheuristics: northern goshawk optimization (NGO), zebra optimization algorithm (ZOA), clouded leopard optimization (CLO), osprey optimization algorithm (OOA), and total interaction algorithm (TIA). The result exhibits that SSHA is better than NGO, ZOA, CLO, OOA, and TIA in 21, 20, 17, 17, and 21 functions. In the second evaluation, the individual search evaluation to compare the contribution between the first and second searches is performed, with the result that the second search outperforms the first search. The third evaluation is performed to assess the contribution of the third search in the optimization process, and the result shows that the contribution of the third search is significant only in three functions. [ABSTRACT FROM AUTHOR]

This paper presents a new metaphor-free metaheuristic search called the swarm bipolar algorithm (SBA). SBA is developed mainly based on the non-free-lunch (NFL) doctrine, which mentions the non-existence of any general optimizer appropriate to answer all varieties of problems. The construction of SBA is based on splitting the swarm into two equal-sized swarms to diversify the searching process while performing intensification within the subswarms. There are two types of finest swarm members: the finest swarm member for the whole swarm and the finest swarm member in every sub-swarm. There are four directed searches performed in every iteration: (1) search toward the finest swarm member, (2) search toward the finest sub-swarm member, (3) search toward the middle between two finest sub-swarm members, and (4) search relative to the randomly picked swarm member from another sub-swarm. The performance of SBA is assessed through two assessments with a set of 23 functions representing the optimization problem. In the benchmark assessment, SBA is contended with five metaheuristics: northern goshawk optimization (NGO), language education optimization (LEO), coati optimization algorithm (COA), fully informed search algorithm (FISA), and total interaction algorithm (TIA). The result presents the superiority of SBA among its contenders by being better than NGO, LEO, COA, FISA, and TIA in 21, 16, 16, 21, and 18 functions. The single search assessment is performed to evaluate each strategy involved in SBA. The result shows that the search toward the middle between the two finest sub-swarm members is the best among the four searches in SBA. [ABSTRACT FROM AUTHOR]

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator's attack on a pufferfish and (ii) exploitation based on the simulation of a predator's escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the coati optimization algorithm (COA) and the osprey optimization algorithm (OOA). ECOA is constructed by five searches performed sequentially by the swarm members. The first three are directed searches, while the last two are neighborhood searches. All three directed searches are adopted from COA and OOA. Meanwhile, the four-bordered neighborhood search is developed based on a new approach. During the assessment, ECOA was challenged to overcome the set of 23 functions and contended with five new metaheuristics: total interaction algorithm (TIA), golden search optimization (GSO), average and subtraction-based optimization (ASBO), COA, and OOA. The result shows that ECOA outperforms TIA, GSO, ASBO, COA, and OOA in 16, 23, 18, 21, and 21 functions. Meanwhile, the individual search test result shows that the directed searches perform better than the neighborhood searches. Moreover, the directed search toward the best member becomes the most dominant search. [ABSTRACT FROM AUTHOR]

There has been a massive development of metaheuristic algorithms in the latest decade where swarm intelligence becomes the fundamental approach. Meanwhile, there is still no ideal metaheuristic that can solve all problems superiorly, as declared in the no-free-lunch (NFL) theory. This work introduces a novel swarm-based metaheuristic named as migration-crossover algorithm (MCA). In MCA, the swarm intelligence is enriched with the crossover technique and the neighbourhood search with unbalanced local search space. The global finest solution becomes the reference in the first step while the middle between two stochastically chosen solutions becomes the reference in the second step. The neighbourhood search is performed in the third step. The collection of 23 functions become the use case during the evaluation of MCA. In the first evaluation, MCA is compared with five new metaheuristics: total interaction algorithm (TIA), osprey optimization algorithm (OOA), migration algorithm (MA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA). The result reveals that MCA is finer than TIA, OOA, MA, COA, and WaOA in 20, 19, 17, 20, and 17 functions subsequently. The result of the second evaluation reveals that the global finest solution becomes the dominant contributor in the high dimension functions while the middle between two stochastically chosen solutions becomes the dominant contributor in the fixed dimension functions. [ABSTRACT FROM AUTHOR]

The Gray Wolf Optimizer (GWO) is a population-based meta-heuristic algorithm that belongs to the family of swarm intelligence algorithms inspired by the social behavior of gray wolves, in particular the social hierarchy and hunting mechanism. Because of its simplicity, flexibility, and few parameters to be tuned, it has been applied to a wide range of optimization problems. And yet it has some disadvantages, such as poor exploration skills, stagnation at local optima, and slow convergence speed. Therefore, different variants of GWO have been proposed and developed to address these disadvantages. In this article, some literature, especially from the last five years, has been reviewed and summarized by well-known publishers. First, the inspiration and the mathematical model of GWO were explained. Subsequently, the improved GWO variants were divided into four categories and discussed. After that, each variant's methodology and experiments were explained and clarified. The study ends with a summary conclusion of the main foundation of GWO and suggests some possible future directions that can be explored further. [ABSTRACT FROM AUTHOR]

Migration algorithm (MA) and walrus optimization algorithm (WaOA) are two new swarm-based metaheuristics which are first introduced in 2023. As new metaheuristics, the modification of these two metaheuristics is still rare. Based on this circumstance, this work constructs a new metaheuristic called as migrating walrus algorithm (MWA) based on the hybridization of both MA and WaOA to create a better metaheuristic than them. MWA consists of five migrations: four directed migrations and one local migration. The references used in these directed migrations are the best walrus, a randomly picked better walrus, a randomly picked walrus, and two randomly picked walruses. In this work, two assessments are carried out: the comparative assessment and the individual migration assessment. The 23 functions are selected as theoretical use cases. In the comparative assessment, MWA is confronted with five new metaheuristics: MA, WaOA, attack leave optimization (ALO), coati optimization algorithm (COA), and osprey optimization algorithm (OOA). The result shows that MWA is better than ALO, COA, MA, OOA, and WaOA in 19, 19, 19, 17, and 17 functions. On the other hand, the individual migration assessment result indicates that the multiple migration approach is important to maintain the superiority of MWA with the directed migration toward the best walrus becoming the most important contributor. This result also strengthens the necessity of the multiple searches strategy rather than a single strategy. [ABSTRACT FROM AUTHOR]

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras' behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications. [ABSTRACT FROM AUTHOR]

This study designs a new stochastic optimization i.e., metaheuristic technique, namely run-catch optimizer (RCO). RCO provides a distinct mechanism regarding the diversification-intensification strategy. Each member runs two sequential activities in every iteration. The first activity is running and the second one is catching. Each activity generates a seed. In the first activity, a virtual best member moves away from the corresponding member to become the first seed. The second seed is generated along the way between the corresponding member and the first seed in the first activity. If both seeds fail to improve, the member conducts a random search to find a new member. Otherwise, the better seed replaces the corresponding member. Then, RCO is challenged to handle both theoretical and real-world optimization problems. The classic 23 functions represent theoretical problems, while the outsourcing optimization problem represents the practical problem. In these simulations, RCO is confronted with five other algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO), marine predator algorithm (MPA), Komodo mlipir algorithm (KMA), and pelican optimization algorithm (POA). The result shows that RCO is better than POA, KMA, MPA, GWO and PSO in optimizing 20, 21, 13, 12, and 21 functions consecutively. Meanwhile, RCO is better than PSO, GWO, and KMA, but worse than MPA and POA in optimizing the outsourcing problem. [ABSTRACT FROM AUTHOR]

This paper promotes a new swarm-based metaheuristic called four directed search algorithm or FDSA. FDSA is designed as a directed search-based metaheuristic without deploying any neighbourhood search. It contains four directed searches that are carried out sequentially. Each search has its reference. These four references are the best member; the resultant of three shuffled members within the swarm; a shuffled member within the swarm; and the resultant of the best member, a shuffled member within the swarm, and the corresponding member. FDSA implements a strict acceptance procedure so that new solution is accepted only if it provides improvement. The investigation is carried out to evaluate the performance of FDSA in solving the 23 functions. FDSA is also confronted with five new metaheuristics: northern goshawk optimization (NGO), average subtraction-based optimization (ASBO), coati optimization algorithm (COA), mixed leader-based optimization (MLBO), and attack leave optimization (ALO). This work also investigates the contribution or dominance of each search in the context of finding the optimal solution. The result shows that FDSA is superior to all these confronters by consecutively outperforming NGO, ASBO, COA, MLBO, and ALO in the 17, 13, 11, 18, and 11 functions. Its superiority is mainly in the high-dimension functions. Through investigation, there is no dominant search among the four directed searches in FDSA. Meanwhile, multiple search strategy is proven to improve performance significantly. On the other hand, the contribution of neighbourhood search is not significant. [ABSTRACT FROM AUTHOR]

This work offers a new stochastic optimization i.e., a metaheuristic algorithm combining both direction-based search and neighbourhood search called as walk-spread algorithm (WSA). These two types of searches become the inspiration for its name where the term walk represents the direction-based search while the term spread represents the neighbourhood search. There are two direction-based searches performed in every iteration where each search produces a single child. Meanwhile, there are two neighbourhood searches performed in every iteration where each search produces several children. The global best unit becomes the first reference while two shuffled units become the second reference in performing the direction-based search. Meanwhile, the local search space of the first neighbourhood search is wide while the second one is narrow. The 23 classic functions are chosen as the assessment of WSA where WSA is confronted with the five latest metaheuristics: mixed leader-based optimization (MLBO), golden search optimization (GSO), pelican optimization algorithm (POA), zebra optimization algorithm (ZOA), and attack-leave optimization (ALO). The assessment result shows that the offered WSA achieves the acceptable result so fast. Moreover, WSA is also superior to these five confronters by outperforming MLBO, GSO, POA, ZOA, and ALO in 23, 23, 22, 21, and 21 functions respectively. [ABSTRACT FROM AUTHOR]

The prediction of skin cancer poses a number of challenges due to the differences in visual characteristics between melanoma, basal cell carcinomas, and squamous cell carcinomas. These visual differences pose difficulties for models in discerning subtle features and patterns accurately. However, a remarkable breakthrough in image analysis using convolutional neural networks (CNNs) has emerged, specifically in the identification of skin cancer from images. Unfortunately, manually designing such neural architectures is prone to errors and consumes substantial time. It has become increasingly popular to design and fine-tune neural networks by using metaheuristic algorithms that are based on natural phenomena. A nature-inspired algorithm is a powerful alternative to traditional algorithms for solving problems, particularly in complex optimization tasks. One such algorithm, the Harris hawk optimization (HHO), has demonstrated promise in automatically identifying the most appropriate solution across a wide range of possibilities, making it suitable for solving complex optimization problems. The purpose of this study is to introduce a novel automated architecture called "HHOForSkin" that combines the power of convolutional neural networks with meta-heuristic optimization techniques. The HHOForSkin framework uses an innovative custom CNN architecture with 26 layers for the analysis of medical images. In addition, a Harris hawk optimization algorithm (HHO) is used to fine-tune the developed model for multiple skin cancer classification problems. The developed model achieves an average accuracy of 99.1% and 98.93% F1 score using a publicly available skin cancer dataset. These results position the developed optimization-based skin cancer detection strategy at the forefront, offering the highest accuracy for seven-class classification problems compared to related works. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]