Population-Oriented Simulated Annealing: A Genetic/Thermodynamic Hybrid Approach to Optimization
Population-Oriented Simulated Annealing: A Genetic/Thermodynamic Hybrid Approach to Optimization
复制标题
面向群体的模拟退火：遗传/热力学混合优化方法
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
J. Cohoon
中科院分区：
文献类型：
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作者：
J. M. Varanelli;J. Cohoon
本文提出了一种新的混合遗传算法（GA）和模拟退火（SA），简称GSA。在该算法中，模拟退火被纳入遗传算法，以摆脱局部最优。分层并行遗传算法的概念，采用并行化GSA的多峰函数的优化。此外，多生态位拥挤被用来维持平行GSA（PGSA）的群体的多样性。所提出的算法的性能进行评估对一组标准的多模态基准函数。与传统的并行遗传算法和繁殖遗传算法相比，多小生境拥挤PGSA和普通PGSA算法的性能有了明显的改善。
In this paper, a new hybrid of genetic algorithm (GA) and simulated annealing (SA), referred to as GSA, is presented. In this algorithm, SA is incorporated into GA to escape from local optima. The concept of hierarchical parallel GA is employed to parallelize GSA for the optimization of multimodal functions. In addition, multi-niche crowding is used to maintain the diversity in the population of the parallel GSA (PGSA). The performance of the proposed algorithms is evaluated against a standard set of multimodal benchmark functions. The multi-niche crowding PGSA and normal PGSA show some remarkable improvement in comparison with the conventional parallel genetic algorithm and the breeder genetic algorithm (BGA).