Abstract
In this paper, a heap-based optimizer algorithm with chaotic search has been presented for the global solution of nonlinear programming problems. Heap-based optimizer (HBO) is a modern human social behavior-influenced algorithm that has been presented as an effective method to solve nonlinear programming problems. One of the difficulties that faces HBO is that it falls into locally optimal solutions and does not reach the global solution. To recompense the disadvantages of such modern algorithm, we integrate a heap-based optimizer with a chaotic search to reach the global optimization for nonlinear programming problems. The proposed algorithm displays the advantages of both modern techniques. The robustness of the proposed algorithm is inspected on a wide scale of different 42 problems including unimodal, multi-modal test problems, and CEC-C06 2019 benchmark problems. The comprehensive results have shown that the proposed algorithm effectively deals with nonlinear programming problems compared with 11 highly cited algorithms in addressing the tasks of optimization. As well as the rapid performance of the proposed algorithm in treating nonlinear programming problems has been proved as the proposed algorithm has taken less time to find the global solution.
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