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Success of metaheuristic algorithms depends on the efficient balance between of exploration and exploitation phases. Any optimization algorithm requires a combination of diverse exploration and proper exploitation to avoid local optima. This paper proposes a new improved version of the Woodpecker Mating Algorithm (WMA), based on opposition-based learning, known as the OWMA aiming to develop exploration and exploitation capacities and establish a simultaneous balance between these two phases. This improvement consists of three major mechanisms, the first of which is the new Distance Opposition-based Learning (DOBL) mechanism for improving exploration, diversity, and convergence. The second mechanism is the allocation of local memory of personal experiences of search agents for developing the exploitation capacity. The third mechanism is the use of a self-regulatory and dynamic method for setting the Hα parameter to improve the Running Away function (RA) performance. The ability of the proposed algorithm to solve 23 benchmark mathematical functions was evaluated and compared to that of a series of the latest and most popular metaheuristic methods reviewed in the research literature. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the classification problem on four biomedical datasets and three function approximation datasets. In addition, the OWMA algorithm was evaluated in five optimization problems constrained by the real world. The simulation results proved the superior and promising performance of the proposed algorithm in the majority of evaluations. The results prove the superiority and promising performance of the proposed algorithm in solving very complicated optimization problems. [ABSTRACT FROM AUTHOR]

Butterfly Optimization Algorithm (BOA) is a newcomer in the category of nature inspired metaheuristic algorithms, inspired from food foraging behavior of the butterflies. Similar to other metaheuristic algorithms, it encounters two probable problems; (1) entrapment in local optima and (2) slow convergence speed. Chaotic maps are one of the best methods to improve the performance of metaheuristic algorithms. In the present study, chaos is introduced into BOA which increases its performance in terms of both local optima avoidance and convergence speed. Ten chaotic maps are employed to enhance the performance of the BOA. The proposed chaotic BOAs are validated on unimodal and multimodal benchmark test functions as well as on engineering design problems. The results indicate that the chaotic maps are able to significantly boost the performance of BOA. [ABSTRACT FROM AUTHOR]

Hybrid Vehicles have experienced major modifications since the last decade. Smart grid success with combination of renewable energy exclusively depends upon the large-scale penetration of Plug-in Hybrid Electric Vehicles (PHEVs) for a sustainable and carbon-free transportation. Recent technical studies regarding various optimization strategies related to PHEV integrated smart grid; such as control and battery charging, vehicle-to-grid (V2G), unit commitment, charging infrastructures, integration of solar and wind energy and demand management prove that electrification of transportation as a rapidly growing field of research. This work presents a holistic review of all substantial research applying metaheuritics optimization for plug-in-hybrid electric vehicles. A summary on future perspective of metaheuristic algorithms is also provided, covering Cuckoo Search (CS), Harmony Search (HS), Artificial Bee Colony (ABC), etc. with a comprehensive reviews on previously applied methods and their performance for solving different real-world problems in the domain of PHEVs. Moreover, significant shifts towards hybrid and hyper metaheuristics are also highlighted. [ABSTRACT FROM AUTHOR]