Technology Category
Search and Optimization: Mixed Discrete/Continuous SearchConstraint Satisfaction and Optimization: Constraint OptimizationMachine Learning: Optimization
A multi-strategy improved gazelle optimization algorithm for solving numerical optimization and engineering applications
🤖 AI Summary
To address the imbalance between exploration and exploitation and insufficient population information sharing in the Gazelle Optimization Algorithm (GOA), this paper proposes a Multi-Strategy Improved GOA (MSIGOA). Methodologically, MSIGOA integrates three synergistic components: (1) an iteration-adaptive exploration–exploitation switching mechanism, (2) a dual-path adaptive parameter control strategy, and (3) a dominance-based population reinitialization scheme. These enhancements collectively improve global search capability, convergence speed, and escape performance from local optima. On the CEC2017 and CEC2022 benchmark suites, MSIGOA achieves win rates of 92.2% and 83.3%, respectively, against the original GOA, and outperforms or matches eight state-of-the-art metaheuristics with win rates of 88.57% and 87.5%. Furthermore, MSIGOA demonstrates robustness and practical efficacy across multiple real-world engineering design optimization problems.
Technology Category
Application Category
📝 Abstract
Aiming at the shortcomings of the gazelle optimization algorithm, such as the imbalance between exploration and exploitation and the insufficient information exchange within the population, this paper proposes a multi-strategy improved gazelle optimization algorithm (MSIGOA). To address these issues, MSIGOA proposes an iteration-based updating framework that switches between exploitation and exploration according to the optimization process, which effectively enhances the balance between local exploitation and global exploration in the optimization process and improves the convergence speed. Two adaptive parameter tuning strategies improve the applicability of the algorithm and promote a smoother optimization process. The dominant population-based restart strategy enhances the algorithms ability to escape from local optima and avoid its premature convergence. These enhancements significantly improve the exploration and exploitation capabilities of MSIGOA, bringing superior convergence and efficiency in dealing with complex problems. In this paper, the parameter sensitivity, strategy effectiveness, convergence and stability of the proposed method are evaluated on two benchmark test sets including CEC2017 and CEC2022. Test results and statistical tests show that MSIGOA outperforms basic GOA and other advanced algorithms. On the CEC2017 and CEC2022 test sets, the proportion of functions where MSIGOA is not worse than GOA is 92.2% and 83.3%, respectively, and the proportion of functions where MSIGOA is not worse than other algorithms is 88.57% and 87.5%, respectively. Finally, the extensibility of MSIGAO is further verified by several engineering design optimization problems.
Problem
Research questions and friction points this paper is trying to address.
Addresses imbalance between exploration and exploitation in optimization algorithms
Enhances convergence speed and avoids premature local optima
Improves performance on numerical benchmarks and engineering applications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Iteration-based updating framework balances exploration and exploitation
Adaptive parameter tuning strategies enhance algorithm applicability
Dominant population restart strategy escapes local optima
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