The Gazelle Optimization Algorithm (GOA) is an innovative metaheuristic inspired by the survival tactics of gazelles in predator-rich environments. While GOA demonstrates notable advantages in solving unimodal, multimodal, and engineering optimization problems, it struggles with local optima and slow convergence in high-dimensional and non-convex scenarios. This paper proposes the Hybrid Gazelle Optimization Algorithm with Differential Evolution (HGOADE), which combines Differential Evolution (DE) with GOA to leverage their complementary strengths for addressing limitations. HGOADE initializes a population of candidate solutions using GOA, then enhances these solutions through DE’s mutation and crossover operations. The algorithm subsequently employs GOA’s exploration and exploitation phases to refine the solutions. By integrating DE’s robust exploration capabilities with GOA’s dynamic search patterns, HGOADE aims to improve global and local search performance. The effectiveness of HGOADE is validated through experiments on benchmark functions from the CEC 2017, CEC 2020, CEC 2022 suite, comparing with ten established optimization techniques, including classical GOA, Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), Arithmetic Optimization Algorithm (AOA), Constriction Coefficient-Based Particle Swarm Optimization Gravitational Search Algorithm (CPSOGSA), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), and DE. Additionally, the performance of HGOADE is assessed against prominent winners from CEC competitions, specifically CMA-ES, LSHADEcnEpSin, and LSHADESPACMA, using the CEC-2017 test suite. Statistical analyses using the Wilcoxon Rank Sum Test and Wilcoxon Signed-Rank Test, along with the Weighted Aggregated Sum Product Assessment (WASPAS) method, confirm that HGOADE significantly outperforms existing algorithms in terms of solution quality and convergence speed. HGOADE’s superiority is validated through six complex engineering design problems, demonstrating its higher feasibility and effectiveness than GOA and other methods. This paper addresses GOA’s shortcomings and advances metaheuristic optimization by integrating DE strategies, offering valuable insights and practical applications for global optimization and engineering design.

中文翻译：

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将差分进化集成到 Gazelle 优化中，用于高级全局优化和工程应用


瞪羚优化算法 （GOA） 是一种创新的元启发式算法，其灵感来自瞪羚在捕食者丰富的环境中的生存策略。虽然 GOA 在解决单峰、多模态和工程优化问题方面表现出显著优势，但它在高维和非凸场景中难以解决局部最优和缓慢收敛。本文提出了差分进化的混合瞪羚优化算法 （HGOADE），该算法将差分进化 （DE） 与 GOA 相结合，以利用它们的互补优势来解决局限性。HGOADE 使用 GOA 初始化一组候选解决方案，然后通过 DE 的突变和交叉操作增强这些解决方案。该算法随后采用 GOA 的探索和利用阶段来优化解决方案。通过将 DE 强大的探索功能与 GOA 的动态搜索模式相结合，HGOADE 旨在提高全球和本地搜索性能。HGOADE 的有效性是通过对 CEC 2017、CEC 2020、CEC 2022 套件的基准函数进行实验验证的，并与十种已建立的优化技术进行比较，包括经典 GOA、Salp 群算法 （SSA）、灰狼优化器 （GWO）、引力搜索算法 （GSA）、算术优化算法 （AOA）、基于收缩系数的粒子群优化引力搜索算法 （CPSOGSA）、正弦余弦算法 （SCA）、 粒子群优化 （PSO）、基于生物地理学的优化 （BBO） 和 DE。此外，使用 CEC-2017 测试套件，根据 CEC 比赛的杰出获胜者（特别是 CMA-ES、LSHADEcnEpsin 和 LSHADESPACMA）评估 HGOADE 的性能。 使用 Wilcoxon 秩和检验和 Wilcoxon 符号秩检验以及加权聚合和乘积评估 （WASPAS） 方法的统计分析证实，HGOADE 在解决方案质量和收敛速度方面明显优于现有算法。HGOADE 的优越性通过六个复杂的工程设计问题得到验证，证明了其比 GOA 等方法更高的可行性和有效性。本文解决了 GOA 的缺点，并通过集成 DE 策略推进了元启发式优化，为全局优化和工程设计提供了有价值的见解和实际应用。