Abstract
Optimization plays an effective role in various disciplines of science and engineering. Optimization problems should either be optimized using the appropriate method (i.e., minimization or maximization). Optimization algorithms are one of the efficient and effective methods in providing quasi-optimal solutions for these type of problems. In this study, a new algorithm called the Mutated Leader Algorithm (MLA) is presented. The main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated leader. In addition to information about the best member of the population, the mutated leader also contains information about the worst member of the population, as well as other normal members of the population. The proposed MLA is mathematically modeled for implementation on optimization problems. A standard set consisting of twenty-three objective functions of different types of unimodal, fixed-dimensional multimodal, and high-dimensional multimodal is used to evaluate the ability of the proposed algorithm in optimization. Also, the results obtained from the MLA are compared with eight well-known algorithms. The results of optimization of objective functions show that the proposed MLA has a high ability to solve various optimization problems. Also, the analysis and comparison of the performance of the proposed MLA against the eight compared algorithms indicates the superiority of the proposed algorithm and ability to provide more suitable quasi-optimal solutions.
Highlights
Nowadays, by enhancing information technology, various number of optimization problems arises in several fields such as bioinformatics, operation research, geophysics, and engineering etc [1,2]
The results of optimization of F1 to F7 objective functions using the proposed Mutated Leader Algorithm (MLA) and eight compared algorithms are presented in Tab. 1
Comparison of the performance of optimization algorithms indicates that the proposed MLA has a high ability to solve unimodal optimization problems
Summary
By enhancing information technology, various number of optimization problems arises in several fields such as bioinformatics, operation research, geophysics, and engineering etc [1,2]. Most real-life optimization problems include large solution space, non-convex, and non-linear objective functions and constraints. Such optimization problems classified as NP-hard problems and have high computational complexity that couldn’t be solved in a polynomial time and complicated in nature. The big disadvantage related to these algorithms is high computational complexity, week constraint-handling capability, problem-specific parameter tuning, and limited problem size [7] These stochastic methods can be categorized into two general groups: heuristics and metaheuristics. The solutions obtained from the optimization algorithms are close to the global optimal and are called quasi-optimal For this reason, various optimization algorithms are introduced by scientists to provide better quasi-optimal solutions to optimization problems.
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