On the efficacy of chaos-enhanced heuristic walks with nature-based controllers for robust and accurate intelligent search, part A: an experimental analysis
Ahmad Mozaffari, Mahdi Emami, Nasser L. Azad, Alireza Fathi · Journal of Experimental & Theoretical Artificial Intelligence · 2014
In this investigation, the authors intend to find out whether chaos-embedded heuristic walks with nature-based controllers are capable of increasing the performance of the standard random walks of evolutionary algorithms. Indeed, the research motivation emanates in the pursuit of addressing an increasing interest in using chaos sequences for modifying the exploitation capability of evolutionary optimisers. Here, the authors propose a nature-based controller to dynamically balance the exploration/exploitation capability of the evolutionary optimiser. To test the validity of the proposed method, a number of well-known Gaussian distribution-based evolutionary operators are considered, and their random parameters are changed with discrete-time chaotic sequences. Besides, to obtain authentic and reliable results regarding the accuracy and robustness of the rival techniques, the authors went through the existing literature and extracted 23 scalable, multimodal, constraint and unconstraint numerical benchmark problems and one engineering problem, i.e. optimal control of shape memory alloy actuators. The convergence rate and computational time of the resulting self-controlled chaos-based evolutionary approaches are analysed to find out whether they are capable of stabilising the chromosomes within a logical time. The results of the numerical experiments indicate that the self-controlled chaos-enhanced heuristic walks significantly increase the global searching capability of the evolutionary optimisers and have an aptitude to show a robust performance.