.png){kind=icon}
Return
An improved swarm optimization algorithm using exploration and evolutionary game theory for efficient exploitation
.png){kind=icon}
PRE
AI
DOI：10.1007/s11227-025-07007-1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Traditional metaheuristic methods often rely on random exploration and exploitation mechanisms, which can lead to inefficient search processes because of a lack of guidance. This paper introduces a novel metaheuristic algorithm that overcomes these limitations using two specialized mechanisms for exploration and exploitation. The population is divided into explorer and exploiter agents, each using distinct strategies. Explorer agents that resemble a swarm follow a trajectory generated through Latin hypercube sampling to efficiently explore the search space. Exploiters use evolutionary game theory, where weaker agents adopt the strategies of stronger ones, ensuring efficient exploitation. This integration of global information enhances diversity, improves the solution quality, and reduces computational costs. Validated against benchmark functions, the proposed algorithm delivered superior results, achieving faster convergence and higher-quality solutions.
Keywords:
Metaheuristic algorithms
Latin hypercube path-tracing
Swarm-guided
Evolutionary game theory
Journal
IF:
2.7
Papers:
1.2K
Citations:
1.0W
Organization
Cited Papers
ShareSaveThe Role of Random Walk-Based Techniques in Enhancing Metaheuristic Optimization Algorithms-A Systematic and Comprehensive Review.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveShareSave
View more