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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]

In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos' digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications. [ABSTRACT FROM AUTHOR]

This paper introduces a new metaheuristic algorithm called Migration Algorithm (MA), which is helpful in solving optimization problems. The fundamental inspiration of MA is the process of human migration, which aims to improve job, educational, economic, and living conditions, and so on. The mathematical modeling of the proposed MA is presented in two phases to empower the proposed approach in exploration and exploitation during the search process. In the exploration phase, the algorithm population is updated based on the simulation of choosing the migration destination among the available options. In the exploitation phase, the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions. MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite. In addition, MA’s results are compared with the performance of twelve well-known metaheuristic algorithms. The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems. The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions. Also, the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications. [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]

In this paper, based on the concept of the NFL theorem, that there is no unique algorithm that has the best performance for all optimization problems, a new human-based metaheuristic algorithm called Language Education Optimization (LEO) is introduced, which is used to solve optimization problems. LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules. LEO is mathematically modeled in three phases: (i) students selecting their teacher, (ii) students learning from each other, and (iii) individual practice, considering exploration in local search and exploitation in local search. The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal, multimodal types and the CEC 2017 test suite. The optimization results show that LEO, with its acceptable ability in exploration, exploitation, and maintaining a balance between them, has efficient performance in optimization applications and solution presentation. LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms. Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior and more competitive in combating the compared algorithms. The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions to optimization problems. The main inspiration for the DA is to simulate the selection of objects from different drawers to create an optimal combination. The optimization process involves a dresser with a given number of drawers, where similar items are placed in each drawer. The optimization is based on selecting suitable items, discarding unsuitable ones from different drawers, and assembling them into an appropriate combination. The DA is described, and its mathematical modeling is presented. The performance of the DA in optimization is tested by solving fifty-two objective functions of various unimodal and multimodal types and the CEC 2017 test suite. The results of the DA are compared to the performance of twelve well-known algorithms. The simulation results demonstrate that the DA, with a proper balance between exploration and exploitation, produces suitable solutions. Furthermore, comparing the performance of optimization algorithms shows that the DA is an effective approach for solving optimization problems and is much more competitive than the twelve algorithms against which it was compared to. Additionally, the implementation of the DA on twenty-two constrained problems from the CEC 2011 test suite demonstrates its high efficiency in handling optimization problems in real-world applications. [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]

Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called "the good, the bad, and the ugly" optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society's movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer's performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new metaheuristic algorithm called the Coati Optimization Algorithm (COA) is introduced, which mimics coati behavior in nature. The fundamental idea of COA is the simulation of the two natural behaviors of coatis: (i) their behavior when attacking and hunting iguanas and (ii) their escape from predators. The implementation steps of COA are described and mathematically modeled in two phases of exploration and exploitation. COA performance is evaluated on fifty-one objective functions, including twenty-nine functions from the IEEE CEC-2017 test suite and twenty-two real-world applications from the IEEE CEC-2011 test suite. COA's results are compared to those of eleven well-known metaheuristic algorithms. The simulation results indicate that COA has an evident superiority over the compared algorithms by balancing exploration in global search and exploitation in local search, and is far more competitive. To assess the COA's effectiveness in real-world applications, the proposed approach is implemented on the IEEE CEC-2011 test functions and four practical optimization problems, which the simulation results indicate the high capability of COA in dealing with these types of optimization problems. • A new optimization algorithm called Coati Optimization Algorithm (COA) is designed to model the natural behaviors of coatis. • COA's fundamental inspirations include attacking and hunting coatis and the escape behavior of coatis when confronted by predators. • The various stages of COA are described, then mathematically modeled in two phases of exploration and exploitation. • Sixty-eight standard benchmark functions have been employed to evaluate COA performance in solving optimization problems. • The performance of the COA in presenting the optimization results is compared with eight well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms. [ABSTRACT FROM AUTHOR]