Abstract
There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.
Highlights
Optimization is defined as finding the best solution out of all possible solutions to a problem by considering the constraints and limitations
In order to analyze the performance of the proposed All Members-Based Optimizer (AMBO) in providing the quasi-optimal solution, AMBO is compared with eight other optimization algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA)
The optimization results for these objective functions using the proposed AMBO and eight other optimization algorithms are presented in Tab. 1
Summary
Optimization is defined as finding the best solution out of all possible solutions to a problem by considering the constraints and limitations. Whatever the quasi-optimal solution provided by an algorithm is closer to the global optimum, that algorithm has a better performance in solving that optimization problem. For this reason, various PBOAs have been introduced by scientists to provide quasi-optimal solutions to optimization problems. Various PBOAs have been introduced by scientists to provide quasi-optimal solutions to optimization problems In this regard, optimization algorithms have been applied in various fields in the literature such as energy [4,5,6,7], protection [8], electrical engineering [9,10,11,12,13,14], and energy carriers [15,16] to achieve the optimal solution
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