Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all others in all classes of optimization search problems, there is a need to constantly find new and/or adapt old search algorithms. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm identified competitive solutions to 23 benchmark unimodal and multimodal test functions compared to the Genetic Algorithm, Particle Swarm Optimization Algorithm and the Gravitational Search Algorithm.
翻译:超光速搜索等简单方法随着搜索算法的解决空间的增加而变得计算成本昂贵和不可靠。由于在优化搜索的所有各类问题中,没有任何方法保证能比所有其他方法发挥更好的效果,因此有必要不断寻找新的和/或调整旧搜索算法。本研究提出了一种从引力搜索算法和梨禽交配行为中得到启发的次声搜索算法。次声搜索算法确定了23个基准单式和多式测试功能的竞争性解决方案,与遗传阿尔戈里特姆、粒子波纹优化阿尔戈里特姆和重力搜索阿尔戈里特姆相比。