Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …

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 …

Attempting to address optimization problems in various scientific disciplines is a
fundamental and significant difficulty requiring optimization. This study presents the …

Hybrid and electric vehicles have been demonstrated as auspicious solutions for ensuring
improvements in fuel saving and emission reductions. From the system design perspective …

This paper introduces a new metaheuristic algorithm named the Osprey Optimization
Algorithm (OOA), which imitates the behavior of osprey in nature. The fundamental …

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

From the past few decades many nature inspired algorithms have been developed and
gaining more popularity because of their effectiveness in solving problems of distinct …

This paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge
based Algorithm (GSK) for solving optimization problems over continuous space. The GSK …

Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …