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Bonobo optimizer (BO): an intelligent heuristic with self-adjusting parameters over continuous spaces and its applications to engineering problems
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Bonobo optimizer (BO): an intelligent heuristic with self-adjusting parameters over continuous spaces and its applications to engineering problems
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Journal Article
Bonobo optimizer (BO): an intelligent heuristic with self-adjusting parameters over continuous spaces and its applications to engineering problems
2022
Overview
In this paper, an intelligent optimization technique, namely Bonobo Optimizer (BO), is proposed. It mimics several interesting reproductive strategies and social behaviour of Bonobos. Bonobos live in a fission-fusion type of social organization, where they form several groups (fission) of different sizes and compositions within the society and move throughout the territory. Afterward, they merge (fusion) again with their society members for conducting specific activities. Bonobos adopt four different reproductive strategies, like restrictive mating, promiscuous mating, extra-group mating, and consortship mating to maintain a proper harmony in the society. These natural strategies are mathematically modeled in the proposed BO to solve an optimization problem. The searching mechanism with self-adjusting controlling parameters of the BO is designed in such a way that it can cope with various situations efficiently, while solving a variety of problems. Moreover, fission-fusion strategy is followed to select the mating partner, which is a unique approach in the literature of meta-heuristics. The performance of BO has been tested on CEC’13 and CEC’14 test functions and compared to that of other efficient and popular optimization algorithms of recent times. The comparisons show some comparable results and statistically superior performances of the proposed BO. Besides these, five complex real-life optimization problems are solved using BO and the results are compared with those reported in the literature. Here also, the performance of BO is found to be either better or comparable than that of others. These results establish the applicability of proposed BO to solve optimization problems.
Publisher
Springer Nature B.V
Subject
