基于信息共享的组合演化算法框架

提出一种用于集成多种启发式算法的通用框架.该框架的每个组成算法各自拥有自属种群并独立演化，因而可以保持各算法的特性和演化过程的连续性.算法间的信息交互仅通过外设的archive结构完成，即每隔一定的迁移间隔，各算法和archive之间进行一定数量的个体迁移.私有种群和信息共享的组合框架可以方便地集成现有的启发式搜索算法，具有很高的普适性.实验选取了五种算法作为子算法，共组成26个组合算法实例，测试了26个组合算法的性能，验证了基于信息共享的组合演化算法框架（EAP_IS）的有效性，并进一步将EAP_IS与其他组合框架进行了对比，实验结果表明，所提出的框架可以有效提高组成算法的综合性能.
Abstract：A general framework for combining multiple evolutionary algorithms EAP_IS is proposed. Each of the constituent algorithms in this framework has its own population to maintain its characteristic and the continuity of the evolution process. EAP_IS runs each constituent algorithm with a part of the given time budget and encourages information sharing among the constituent algorithms. The effectiveness of EAP_IS has been verified by investigating 26 instantiations of it on 25 benchmark functions, and further comparisons of EAP_IS with other combinatorial frameworks have been conducted. Experimental results show that the proposed framework can improve the performance of constituent algorithms effectively.