Open Access

ARTICLE

MLA: A New Mutated Leader Algorithm for Solving Optimization Problems

Fatemeh Ahmadi Zeidabadi¹, Sajjad Amiri Doumari¹, Mohammad Dehghani², Zeinab Montazeri³, Pavel Trojovský^4,*, Gaurav Dhiman⁵

1 Department of Mathematics and Computer Sciences, Sirjan University of Technology, Sirjan, Iran
2 Graduate of Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
3 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
4 Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03, Hradec Králové, Czech Republic
5 Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab, India

* Corresponding Author: Pavel Trojovský. Email:

(This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)

Computers, Materials & Continua 2022, 70(3), 5631-5649. https://doi.org/10.32604/cmc.2022.021072

Received 22 June 2021; Accepted 23 July 2021; Issue published 11 October 2021

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.

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Citations