Hybrid improved sine cosine algorithm for mixed-integer nonlinear programming problems

This paper presents the development and application of the JADESCA optimization algorithm for solving complex engineering design problems, including the welded beam, pressure vessel, spring, and speed reducer design problems. JADESCA, a hybrid algorithm that combines elements of JADE (differential evolution with adaptive parameters) and the sine cosine algorithm (SCA), is evaluated against a range of benchmark functions from the CEC2022 competition as well as specific engineering problems. The algorithm’s performance is analyzed through convergence curves, search history diagrams, and statistical results. In engineering design problems, JADESCA consistently demonstrates superior performance by achieving optimal or near-optimal solutions with high precision and consistency. In particular, JADESCA outperforms 25 state-of-the-art optimizers over the CEC2022 benchmark functions, further proving its robustness and adaptability. Statistical comparisons and Wilcoxon rank-sum tests reinforce the superiority of JADESCA in achieving competitive results across various test cases, solidifying its effectiveness in handling complex, constrained optimization problems for engineering applications.

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Efficiently handling constraints in mixed-integer nonlinear programming problems using gradient-based repair differential evolution

Molina-Pérez,

Portilla-Flores,

Mezura-Montes

et al. 2024

PeerJ Computer Science

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Mixed integer nonlinear programming (MINLP) addresses optimization problems that involve continuous and discrete/integer decision variables, as well as nonlinear functions. These problems often exhibit multiple discontinuous feasible parts due to the presence of integer variables. Discontinuous feasible parts can be analyzed as subproblems, some of which may be highly constrained. This significantly impacts the performance of evolutionary algorithms (EAs), whose operators are generally insensitive to constraints, leading to the generation of numerous infeasible solutions. In this article, a variant of the differential evolution algorithm (DE) with a gradient-based repair method for MINLP problems (G-DEmi) is proposed. The aim of the repair method is to fix promising infeasible solutions in different subproblems using the gradient information of the constraint set. Extensive experiments were conducted to evaluate the performance of G-DEmi on a set of MINLP benchmark problems and a real-world case. The results demonstrated that G-DEmi outperformed several state-of-the-art algorithms. Notably, G-DEmi did not require novel improvement strategies in the variation operators to promote diversity; instead, an effective exploration within each subproblem is under consideration. Furthermore, the gradient-based repair method was successfully extended to other DE variants, emphasizing its capacity in a more general context.

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Hybrid improved sine cosine algorithm for mixed-integer nonlinear programming problems

Cited by 3 publications

References 56 publications

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Efficiently handling constraints in mixed-integer nonlinear programming problems using gradient-based repair differential evolution

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