Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

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

This paper presents a novel meta-heuristic algorithm so-called White Shark Optimizer
(WSO) to solve optimization problems over a continuous search space. The core ideas and …

This study proposes a new metaheuristic algorithm called sand cat swarm optimization
(SCSO) which mimics the sand cat behavior that tries to survive in nature. These cats are …

This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search
Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of …

This paper proposes a meta heuristic optimization algorithm, called Crayfish Optimization
Algorithm (COA), which simulates crayfish's summer resort behavior, competition behavior …

The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers.
Most of these cliché methods mimic animals' searching trends and possess a small …

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 …

A recent set of overused population-based methods have been published in recent years.
Despite their popularity, most of them have uncertain, immature performance, partially done …

This paper proposes a new metaheuristic optimization algorithm based on ancient war
strategy. The proposed War Strategy Optimization (WSO) is based on the strategic …