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A novel multi-objective optimization algorithm based on artificial algae for multi-objective engineering design problems

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APPLIED INTELLIGENCE 2018年第10期48卷 3762-3781页

作者： Tawhid, Mohamed A. Savsani, Vimal Thompson Rivers Univ Dept Math & Stat Fac Sci Kamloops BC V2C 0C8 Canada Alexandria Univ Dept Math & Comp Sci Fac Sci Moharam Bey 21511 Alexandria Egypt Pandit Deendayal Petr Univ Dept Mech Engn Gandinagar Gujarat India

There are many diverse fields and applications such as data mining, engineering, operations research, economics, and science can be formulated as multi-objective optimization problems. In this paper, we describe and propose a novel and a useful multi-objective artificial algae algorithm (MO-AAA) to solve multi-objective engineering design problems. Our proposed algorithm, (MO-AAA), is based on the search technique of artificial algae algorithm(AAA) algorithm. MO-ADA applies the elitist non-dominated sorting and crowding distance approach to preserve the diversity among the optimal set of solutions and obtains various non-domination levels, respectively. Also, we evaluate the effectiveness of the proposed algorithm by applying it on different multi-objective benchmark problems (20 challenging benchmark problems from CEC 2009 for unconstrained and constrained multi-objective optimization problems) and engineering design benchmark problems with distinctive features. Finally, our results show that MO-AAA efficiently generates the Pareto front and is easy to implement, promising and competitive compared to other state-of-the-art algorithms considered in this work.

关键词： multi-objective optimization Artificial algae algorithm multi-objective engineering design problems Pareto optimal solutions

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multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems

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NEURAL COMPUTING & APPLICATIONS 2019年第2-Sup期31卷 915-929页

作者： Tawhid, Mohamed A. Savsani, Vimal Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada Alexandria Univ Fac Sci Dept Math & Comp Sci Alexandria Egypt Pandit Deendayal Petr Univ Dept Mech Engn Gandhinagar Gujarat India

This paper proposes a novel and an effective multi-objective optimization algorithm named multi-objective sine-cosine algorithm (MO-SCA) which is based on the search technique of sine-cosine algorithm (SCA). MO-SCA employs the elitist non-dominated sorting and crowding distance approach for obtaining different non-domination levels and to preserve the diversity among the optimal set of solutions, respectively. The effectiveness of the method is measured by implementing it on multi-objective benchmark problems that have various characteristics of Pareto front such as convex, non-convex and discrete. This proposed algorithm is also checked for the multi-objective engineering design problems with distinctive features. Furthermore, we show the proposed algorithm effectively generates the Pareto front and is easy to implement and algorithmically simple.

关键词： multi-objective optimization Sine-cosine algorithm multi-objective engineering design problems

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Non-dominated sorting moth flame optimization (NS-MFO) for multi-objective problems

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engineering APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 63卷 20-32页

作者： Savsani, Vimal Tawhid, Mohamed A. Pandit Deendayal Petr Univ Gandinagar Gujarat India Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada Alexandria Univ Fac Sci Dept Math & Comp Sci Alexandria 21511 Egypt

This paper proposes an effective non-dominated moth-flame optimization algorithm (NS-MFO) method for solving multi-objective problems. Most of the multi-objective optimization algorithms use different search techniques inspired by different optimization techniques such as genetic algorithms, differential evolutions, particle swarm optimization, cuckoo search etc., but search techniques of recently developed metaheuristics have hardly been investigated. Non-dominated moth-flame optimization algorithm (NSMFO) is based on the search technique of moth-flame optimization algorithm (MFO) algorithm and utilizes the elitist non-dominated sorting and crowding distance approach for obtaining different non domination levels and to preserve the diversity among the optimal set of solutions respectively. The effectiveness of the method is measured by implementing it on multi-objective benchmark problems and multi-objective engineering design problems with distinctive features. It is shown in this paper that this method effectively generates the Pareto front and also, this method is easy to implement and algorithmically simple. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization Moth-flame optimization algorithm multi-objective engineering design problems

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NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-38页

作者： Huang, Huajuan Zheng, Baofeng Wei, Xiuxi Zhou, Yongquan Zhang, Yuedong Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Minzu Univ Coll Elect Informat Nanning 530006 Peoples R China Guangxi Minzu Univ Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems.

关键词： multi-objective optimization Meta-heuristic Chicken swarm optimization algorithm Fast non-dominated sorting multi-objective engineering design problems

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