Abstract
This paper offers a novel multiobjective approach – Multiobjective Ions Motion Optimization (MOIMO) algorithm stimulated by the movements of ions in nature. The main inspiration behind this approach is the force of attraction and repulsion between anions and cations. A storage and leader selection strategy is combined with the single objective Ions Motion Optimization (IMO) approach to estimate the Pareto optimum front for multiobjective optimization. The proposed method is applied to 18 different benchmark test functions to confirm its efficiency in finding optimal solutions. The outcomes are compared with three novel and well-accepted techniques in the literature using five performance parameters quantitatively and obtained Pareto fronts qualitatively. The comparison proves that MOIMO can approximate Pareto optimal solutions with good convergence and coverage with minimum computational time.
Highlights
The optimization process looks for finding the minimum or maximum value for single or multiple objectives
This paper offers a novel multiobjective approach – Multiobjective Ions Motion Optimization (MOIMO) algorithm stimulated by the movements of ions in nature
A storage and leader selection strategy were combined into a single objective Ions Motion Algorithm (IMO) approach to solving multiobjective problems
Summary
The optimization process looks for finding the minimum or maximum value for single or multiple objectives. Multiobjective optimization is much more complicated than single-objective optimization because of the existence of multiple optimum solutions. Mirjalili, Gandomi, et al 2017) and Non-dominated Sorting Ions Motion Algorithm (Buch and Trivedi 2020) All these algorithms have proved their efficiency in solving the multiobjective problem. Even the computational difficulty is lesser than several optimization procedures reported in the literature Such commanding features inspired us to develop a multiobjective version of the existing single objective IMO. Lib and Uib give lower and upper limits of the variable Such kind of problems foil us from equating results utilizing the relational operators as there are multiple criteria to evaluate solutions. The overall outlines of all population-based multiobjective algorithms nearly match They begin the optimization procedure with multiple candidate solutions. We present the multiobjective form of singleobjective IMO
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