A Hybrid JADE–Sine Cosine Approach for Advanced Metaheuristic Optimization
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms · Evolutionary Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Data Science and Artificial Intelligence, Faculty of Information Technology, University of Petra, Amman 11932, Jordan
Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan
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