关键词: 启发式算法, 群智能优化计算, 进化计算, Lotka-Volterra生态平衡动力学模型

Abstract:  To solve some complicated function optimization problems, this paper proposes the ecological balance dynamics-based optimization (EBDO) algorithm based on the Lotka-Volterra ecological balance dynamics. The algorithm supposes that some types of autotrophy (such as plants), consumer (such as animals) and decomposer population exist in the ecosystem, consumer populations take autotrophy populations as food; decomposer populations take dead consumer populations as food and provide nutrition for autotrophy populations. This paper constructs the consumer-autotrophy operator, autotrophy-decomposer operator and decomposer-consumer operator by using the above relationship between ecosystem phenomena. The growth of autotrophy, consumer and decomposer populations is equivalent to alternative solutions transferring from a position to another in the search space of an optimization problem. Results show that the algorithm has the characteristics of strong search capability and global convergence, and has a high convergence speed for the complicated function optimization problems.

Key words: heuristic algorithm, swarm intelligence optimization computation, evolution computation, Lotka-Volterra ecological balance dynamics model