A Modified Equilibrium Optimizer Using Opposition-Based Learning and Teaching-Learning Strategy

Equilibrium Optimizer (EO) is a newly developed intelligent optimization algorithm inspired by control volume mass balance models. EO has been proven to have an excellent solution effect on some optimization problems, with the advantages of ease of implementation and strong adaptability. However, the original EO has some disadvantages when solving complex multimodal problems, including an immature balance between exploration and exploitation, the high probability of falling into local optima entrapment, and the slow rate of convergence. In order to address these shortcomings, a modified equilibrium optimizer (OTLEO) is proposed using teaching-learning-based optimization (TLBO) and opposition-based learning (OBL). These modifications aim to maintain the diversity of the solutions, expand the algorithm’s search range, improve exploration and exploitation capabilities, and avoid local optima. To verify and analyze the modified equilibrium optimizer algorithm’s performance, the OTLEO was tested for 32 classical benchmark functions. All algorithms are independently run 30 times in the same environment. Thereafter, the comparative evaluation against the OTLEO and other six representative algorithms is conducted. Four real-world engineering application problems, including multiple sequence alignment and so on, are used for additional validation. The experimental results, statistical tests, qualitative analysis, and stability analysis demonstrate that the proposed OTLEO outperforms the original EO and other algorithms.

Abstract licence: CC BY-NC-ND

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