This paper proposes a new multi-algorithm based evolution strategy with the addition of
adaptive mutation operators for global optimization. The new algorithm namely Kepler …

This paper presents a study focused on enhancing the effectiveness of cuckoo search (CS).
The goal is to improve its performance in avoiding local optima, improve the exploration and …

Differential evolution (DE) is a robust optimizer designed for solving complex domain
research problems in the computational intelligence community. In the present work, a multi …

This paper proposes a novel social cognitive learning-based metaheuristic called kids
Learning Optimizer (KLO), inspired by the early social learning behavior of kids organized …

A multi-hybrid algorithm is proposed in this paper based on the Kepler Optimization
algorithm (KOA), Red Panda Optimization (RPO), Meerkat Optimization (MO), and Grey Wolf …

Abstract The Grey Wolf Optimization (GWO) is a highly effective meta-heuristic algorithm
leveraging swarm intelligence to tackle real-world optimization problems. However, when …

This article introduces a novel single objective and a multi-objective multi-hybrid naked mole-
rat (moIGDN) algorithm optimization techniques for solving numerical benchmarks and …

In this paper, an adaptive switching anti-disturbance attitude control scheme based on
improved snake optimizer (SO) is proposed for quadrotor attitude control when a quadrotor …

Path planning is a critical component of autonomous unmanned aerial vehicle (UAV)
navigation systems, yet traditional and sampling-based methods encounter limitations in …

Teaching Learning Based Optimization Algorithm, nature inspired optimization technique
based on presence of constraints, TLBO can be used and adapted to solve constrained and …