Abstract
Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms’ performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.
Highlights
The technique of finding the optimal solution among all possible solutions to a problem is known as optimization
Simulations of ants’ behavior when searching for food have been used in the design of the Ant Colony Optimization (ACO) algorithm [23], modeling of the cooling process of metals during metalworking has been used in the design of the Simulated Annealing (SA) algorithm [24], and simulation of the human immune system against viruses have been used in the design of the Artificial Immune System (AIS) algorithm [25]
The Archery Algorithm (AA) findings are compared to the performance of eight optimization techniques, including Particle Swarm Optimization (PSO) [26], Teaching-Learning Based Optimization (TLBO) [27], Grey Wolf Optimization (GWO) [28], Whale Optimization Algorithm (WOA) [29], Marine Predators Algorithm (MPA) [30], Tunicate Swarm Algorithm (TSA) [41] Gravitational Search Algorithm (GSA) [49], and Genetic Algorithm (GA) [54]
Summary
The technique of finding the optimal solution among all possible solutions to a problem is known as optimization. Stochastic approaches, which are based on random search in the problem-solving space, can yield reasonable and acceptable solutions to optimization problems [4]. Optimization algorithms are one of the most extensively used stochastic approaches for addressing optimization problems that do not need the use of objective function gradient and derivative information. The important issue with optimization algorithms is that because they are stochastic methods, there is no guarantee that their provided solutions be global optimal. When the performance of different optimization algorithms on solving an optimization issue is compared, the algorithm that is capable of providing a quasi-optimal solution that is closer to the global optimal is the superior algorithm. The contribution of this study is the development of a novel optimization method known as Archery Algorithm (AA) that provides quasi-optimal solutions to optimization problems.
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