As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …

Research in metaheuristics for global optimization problems are currently experiencing an
overload of wide range of available metaheuristic-based solution approaches. Since the …

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in
optimizing and enhancing efficiency across diverse models and systems, renowned for their …

Currently, meta-heuristic algorithms have been widely studied and applied, but balancing
exploration and exploitation remains a challenge. In this study, a novel meta-heuristic …

A new bio-inspired optimization algorithm called artificial hummingbird algorithm (AHA) is
proposed in this work to solve optimization problems. The AHA algorithm simulates the …

This paper proposes a novel population-based optimization method, called Aquila Optimizer
(AO), which is inspired by the Aquila's behaviors in nature during the process of catching the …

The development of efficient optimization algorithms is crucial across various scientific
disciplines. As the complexity and diversity of optimization problems continue to grow …

This paper introduces the Human Evolutionary Optimization Algorithm (HEOA), a
metaheuristic algorithm inspired by human evolution. HEOA divides the global search …

This study presents a new nature-inspired optimization algorithm, namely the Mantis Search
Algorithm (MSA), inspired by the unique hunting behavior and sexual cannibalism of praying …

As an important branch of production scheduling, flexible job-shop scheduling problem
(FJSP) is difficult to solve and is proven to be NP-hard. Many intelligent algorithms have …