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Chaotic evolution optimization: A novel metaheuristic algorithm inspired by chaotic dynamics
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AI
DOI：10.1016/j.chaos.2025.116049.png)
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Abstract
En 中文
In this paper, a novel population-based metaheuristic algorithm inspired by chaotic dynamics, called chaotic evolution optimization (CEO), is proposed. The main inspiration for CEO is derived from the chaotic evolution process of a two-dimensional discrete memristive map. By leveraging the hyperchaotic properties of the memristive map, the CEO algorithm is mathematically modeled to introduce random search directions for evolutionary processes. Then, the CEO is developed by integrating the crossover and mutation operations from the differential evolution (DE) framework. The proposed algorithm is evaluated by conducting experiments on 15 benchmark test problems and a sensor network localization problem, comparing its performance with 12 other metaheuristic algorithms. Experimental results demonstrate that CEO exhibits highly promising and competitive performance in comparison to widely used, classical, and well-established metaheuristic algorithms. Moreover, CEO effectively addresses the zero-bias problem observed in many recently proposed algorithms. The source code for CEO algorithm will publicly available at: https://github.com/Running-Wolf1010/CEO.
Keywords:
Chaotic evolution optimization
Metaheuristic
Optimization
Discrete memristor
Hyperchaos