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Enhanced Single-Objective Optimization Algorithm with Progressive Exploration Strategy for RF Accelerating Structure Optimization
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DOI：10.3390/app15189965.png)
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Abstract
En 中文
In many engineering applications, multi-objective optimization problems can be reformulated as single-objective problems with multiple constraints to improve computational efficiency. This paper discusses the characteristics and challenges of RF accelerating structure optimizations and proposes an enhanced single-objective optimization strategy based on progressive exploration method to find the global optimal solution within a large solution space characterized by a continuous and confined distribution of feasible solutions. It begins from an arbitrary feasible solution and progressively slides and expands the solution space fragment along the distribution path of feasible solutions to rapidly explore the entire space. By incorporating a re-initialization mechanism to enhance swarm diversity and introducing penalty factors in place of constraints to increase the number of feasible solutions, the algorithm significantly improves its ability to escape local optima traps. The proposed algorithm is applied to optimize a DAA structure, yielding satisfactory results and convergence speed. These results highlight the method's effectiveness and its potential applicability to other complex constrained optimization problems.
Keywords:
swarm intelligence
single-objective optimization
RF accelerating structure
structure optimization
progressive exploration strategy
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