The grey wolf optimization (GWO) algorithm is widely utilized in many global applications. In this paper, a dynamic opposite learning-assisted optimizer (DOLGWO) was proposed to improve the search ability. Herein, learning (DOL) strategy adopted, which has an asymmetric space and can adjust with random point enhance exploitation exploration capabilities. To validate performance of DOLGWO algori...

Minimizing energy costs while maintaining consumer satisfaction is a very challenging task in smart home. The contradictory nature of these two objective functions (cost and level) requires multi-objective problem formulation that can offer several trade-off solutions to the consumer. Previous works have individually considered cost satisfaction, but there lack research considers both objective...

Metaheuristic algorithms are widely used for optimization in both research and the industrial community simplicity, flexibility, robustness. However, multi-modal is a difficult task, even metaheuristic algorithms. Two important issues that need to be handled solving problems (a) categorize multiple local/global optima (b) uphold these till ending. Besides, robust local search ability also prere...

Grey Wolf Optimizer (GWO) is a nature-inspired swarm intelligence algorithm that mimics the hunting behavior of grey wolves. GWO, in its basic form, real coded needs modifications to deal with binary optimization problems. In this paper, previous work on binarization GWO are reviewed, and classified respect their encoding scheme, updating strategy, transfer function. Then, we propose novel (nam...