This study investigates the problem of swarm robots searching for multiple targets in an unknown environment. We propose Historical Optimal Weighting Grey Wolf Optimization (HOWGWO) algorithm based on improved grouping strategy. In HOWGWO algorithm, we gather and update every individual grey wolf’s historical optimal position rank wolves merit their position. The prey is dynamically estimated b...

A modified variant of gray wolf optimization algorithm, namely, mean gray wolf optimization algorithm has been developed by modifying the position update (encircling behavior) equations of gray wolf optimization algorithm. The proposed variant has been tested on 23 standard benchmark well-known test functions (unimodal, multimodal, and fixed-dimension multimodal), and the performance of modifie...

Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type network, training and selecting parameters that define networks architecture play an important role enhancing the accuracy network's output; Therefore, Prior training, those must be optimized. Grey Wolf Optimizer (GWO) has been considered one efficient developed approaches Swarm Intellige...

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...

Opportunistic routing has increased the efficiency and reliability of Cognitive Radio Ad-Hoc Networks (CRAHN). Many researchers have developed opportunistic models, among them Spectrum Map-empowered Routing (SMOR) model, which is considered a more efficient model in this field. However, there are certain limitations SMOR, require attention resolution. The issue delay degradation packet delivery...

In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the perform...