Open Access

ARTICLE

AMBO: All Members-Based Optimizer for Solving Optimization Problems

Fatemeh Ahmadi Zeidabadi¹, Sajjad Amiri Doumari¹, Mohammad Dehghani², Zeinab Montazeri², Pavel Trojovský^3,*, Gaurav Dhiman⁴

1 Department of Mathematics and Computer Sciences, Sirjan University of Technology, Sirjan, Iran
2 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
3 Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Králové, 50003, Czech Republic
4 Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab, India

* Corresponding Author: Pavel Trojovský. Email:

(This article belongs to the Special Issue: Integrity and Multimedia Data Management in Healthcare Applications using IoT)

Computers, Materials & Continua 2022, 70(2), 2905-2921. https://doi.org/10.32604/cmc.2022.019867

Received 29 April 2021; Accepted 18 June 2021; Issue published 27 September 2021

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.

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Citations