Multi-objective particle swarm optimization with random immigrants
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ORIGINAL ARTICLE
Multi-objective particle swarm optimization with random immigrants Ali Nadi Ünal1
· Gülgün Kayakutlu2
Received: 25 November 2017 / Accepted: 23 May 2020 © The Author(s) 2020
Abstract Complex problems of the current business world need new approaches and new computational algorithms for solution. Majority of the issues need analysis from different angles, and hence, multi-objective solutions are more widely used. One of the recently well-accepted computational algorithms is Multi-objective Particle Swarm Optimization (MOPSO). This is an easily implemented and high time performance nature-inspired approach; however, the best solutions are not found for archiving, solution updating, and fast convergence problems faced in certain cases. This study investigates the previously proposed solutions for creating diversity in using MOPSO and proposes using random immigrants approach. Application of the proposed solution is tested in four different sets using Generational Distance, Spacing, Error Ratio, and Run Time performance measures. The achieved results are statistically tested against mutation-based diversity for all four performance metrics. Advantages of this new approach will support the metaheuristic researchers. Keywords Metaheuristics · Multi-objective optimization · Particle swarm optimization · Random immigrants
Introduction Nature-inspired optimization methods have been used effectively to solve a wide variety of complex problems that consist of both single and multiple objective search domains. Among these methods, swarm intelligence is a promising research area. Introduced to solve single objective problems, Particle Swarm Optimization (PSO) [15] has attracted many researchers in metaheuristic optimization area, and started to gain prominence at solving multiple objective problems not more than 5 years after its introduction (see [27] for the first attempt on multi-objective optimization). This is because of the relative simplicity and the success as a single-objective optimizer, as well as high speed of convergence [4,22]. Furthermore, due to its population based nature, it enables to obtain a set of trade-off solutions in a single run, unlike the traditional techniques which employ a series of separate runs [36].
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Ali Nadi Ünal [email protected] Gülgün Kayakutlu [email protected]
1
Hezarfen Aeronautics and Space Technologies Institute, National Defense University, Istanbul, Turkey
2
Energy Institute, Istanbul Technical University, Istanbul, Turkey
However, there still exist three main issues to be considered in Multi-objective Particle Swarm Optimization (MOPSO): (1) archive maintenance, (2) process to update global best and individual best, and (3) solutions for local optima and premature convergence problems [11,13]. Maintaining an external archive, which is used to keep a historical record of non-dominated solutions in accordance with a quality measure, serves the main purpose of multi-objective optimization. Computational cost and memory size considerations cause keeping
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