This paper reviews the application of metaheuristics for optimized sustainable supply chain
management (SSCM). This paper explores the potential of metaheuristics to improve the …

Uncertainty and imprecision accompany the world we live in and occur in almost every
event. How to better interpret and manage uncertainty and imprecision play a vital role in …

Accurate and reliable short-term forecasting of wind power is vital for balancing energy and
integrating wind power into a grid. A novel hybrid deep learning model is designed in this …

An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging
optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the …

Computationally efficient algorithms for large-scale black-box optimization have become
increasingly important in recent years due to the growing complexity of engineering and …

An original math-inspired meta-heuristic algorithm, named quadratic interpolation
optimization (QIO), is proposed to address numerical optimization and engineering issues …

In recent algorithmic family simulates different biological processes observed in Nature in
order to efficiently address complex optimization problems. In the last years the number of …

Metaheuristic optimization algorithms are global optimization approaches that manage the
search process to efficiently explore search spaces associated with different optimization …

Abstract Prairie Dog Optimization is a population-based optimization method that uses the
behavior of prairie dogs to find the optimal solution. This paper proposes a novel …

Feature Selection (FS) is considered a crucial procedure for eliminating unnecessary
features from datasets. FS is considered a challenging problem that is difficult to solve …