Two-phase shuffled frog-leaping algorithm
Bhagyashri Naruka, Tarun Kumar Sharma, Millie Pant, Jitendra Rajpurohit, Shweta Sharma · 2014
Shuffled frog-leaping algorithm (SFLA) is a recent addition to the stochastic search methods that mimics the social and natural behaviour of species. The basic idea behind modelling of such algorithms is to achieve comparatively better solutions to the multifaceted optimization problems that are not easy to solve using traditional or deterministic mathematical techniques. SFLA combines the advantages of particle swarm optimization (PSO) and genetic algorithm (GA). In this study to improve the convergence speed, two modifications have been proposed firstly, initial population is generated using opposition based learning and secondly search process of SFLA is improved using scaling factor. The proposed algorithm is named as Two-Phase SFLA. The impact of the proposal is illustrated on four structural engineering design problems.