A Modified Bonobo Optimizer With Its Application in Solving Engineering Design Problems

This paper presents a modified bonobo optimizer (MBO) that integrates the Gaussian local mutation, restart strategy, and random contraction strategy into the original bonobo optimizer (BO). BO, inspired by the unique reproductive schemes and fission-fusion social behaviors of bonobos, has previously demonstrated promising results in solving a range of optimization problems. With the new modifications, MBO seeks to improve exploration and exploitation abilities, achieving enhanced convergence speed and solution quality. The Gaussian local mutation aids in fine-tuning solutions by introducing localized variations, the restart strategy provides a mechanism to escape potential local optima, while the random contraction strategy ensures better global search capabilities. The enhanced MBO’s performance is critically assessed on the 10 and 100-dimensional CEC 2017 and 10 and 20-dimensional CEC 2022 benchmark suites, along with seven engineering optimization problems, including cantilever beam design, industrial refrigeration system design, welded beam design, speed reducer design, pressure vessel design, multi-product batch plant design, and three-bar truss design. The MBO algorithm exhibits significant improvements in optimization performance, evidenced by highly significant p-values (as low as 1.25E-11) in the Wilcoxon’s Signed Rank Test. Preliminary results indicate that the MBO exhibits a marked improvement in both solution accuracy and robustness over its predecessor and other state-of-the-art optimization algorithms such as original bonobo optimizer, sand cat swarm optimization, Chernobyl disaster optimizer, driving training-based optimization, Harris hawk optimizer, Archimedes optimization algorithm, smell agent optimizer, grasshopper optimization algorithm, particle swarm optimization, hybrid sine cosine algorithm with differential evolution, modified capuchin search algorithm, liver cancer algorithm, and modified chameleon swarm algorithm. The algorithm’s robust performance can be attributed to its accelerated convergence rate, stability across diverse functions, good exploration-exploitation behavior, and adaptability to high-dimensional and complex solution spaces. The systematic enhancement of proposed algorithm’s convergence capabilities positions it as a reliable and efficient tool for addressing challenging engineering optimization problems.

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