A nature inspired hybrid optimisation algorithm for dynamic environment with real parameter encoding. (2017)

• Record Type: Journal Article
• Authors: Tripathi, Ashish; Saxena, Nitin; Mishra, K.K.; Misra, A.K.
• Is Part Of: International journal of bio-inspired computation. Volume 10:Number 1(2017)
• Page Start: 24
• Page End: 32
• Publication Date: 2017
• Subjects: adaptive learning -- environmental adaption method for dynamic -- EAMD -- hybrid algorithm -- environmental adaption method -- EAM -- optimisation -- PSO; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

In recent years, many nature inspired algorithms have been proposed which are widely applicable for different optimisation problems. Real-world optimisation problems have become more complex and dynamic in nature and a single optimisation algorithm is not good enough to solve such type of problems individually. Thus hybridisation of two or more algorithms may be a fruitful effort in handling the limitations of individual algorithm. In this paper a hybrid optimisation algorithm has been established which includes the features of environmental adaption method for dynamic (EAMD) environment and particle swarm optimisation (PSO). This algorithm is specially designed to optimise both unimodal and multimodal problems and the performance is checked over a group of 24 benchmark functions provided by black box optimisation benchmarking (BBOB-2013). The result shows the superiority of this hybrid algorithm over other well established state-of-the-art algorithms.