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Multi-objective Bonobo Optimizer (MOBO): an intelligent heuristic for multi-criteria optimization

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Abstract

Non-traditional optimization tools have proved their potential in solving various types of optimization problems. These problems deal with either single objective or multiple/many objectives. Bonobo Optimizer (BO) is an intelligent and adaptive metaheuristic optimization algorithm inspired from the social behavior and reproductive strategies of bonobos. There is no study in the literature to extend this BO to solve multi-objective optimization problems. This paper presents a Multi-objective Bonobo Optimizer (MOBO) to solve different optimization problems. Three different versions of MOBO are proposed in this paper, each using a different method, such as non-dominated sorting with adaptation of grid approach; a ranking scheme for sorting of population with crowding distance approach; decomposition technique, wherein the solutions are obtained by dividing a multi-objective problem into a number of single-objective problems. The performances of all three different versions of the proposed MOBO had been tested on a set of thirty diversified benchmark test functions, and the results were compared with that of four other well-known multi-objective optimization techniques available in the literature. The obtained results showed that the first two versions of the proposed algorithms either outperformed or performed competitively in terms of convergence and diversity compared to the others. However, the third version of the proposed techniques was found to have the poor performance.

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  1. Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Amit Kumar Das, Siva Vignesh Krishnan & Dilip Kumar Pratihar

  2. Department of Mechanical Engineering, SVNIT, Surat, 395007, India

    Ankit Kumar Nikum

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Das, A.K., Nikum, A.K., Krishnan, S.V. et al. Multi-objective Bonobo Optimizer (MOBO): an intelligent heuristic for multi-criteria optimization. Knowl Inf Syst 62, 4407–4444 (2020). https://doi.org/10.1007/s10115-020-01503-x

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