Benchmark suites provide useful measurements of an evolutionary algorithm's problem-
solving capacity, but the constituent problems are often too complex to cleanly identify an …

Global optimization of complex and high-dimensional functions remains a central challenge
with broad applications in science and engineering. This study introduces a new …

FEREBUS is a Gaussian process regression (GPR) engine embedded in the large
machinery of FFLUX, a novel machine learnt force field developed from scratch through …

Analyzing stochastic algorithms for comprehensive performance and comparison across
diverse contexts is essential. By evaluating and adjusting algorithm effectiveness across a …

Seismic analysis of earth slopes is one of the critical concerns in the field of civil
engineering. In the current study, a pseudo-static limit equilibrium approach is applied for …

In this work, a new metaheuristic algorithm, namely the hybrid pelican Komodo algorithm
(HPKA), has been proposed. This algorithm is developed by hybridizing two shortcoming …

This study introduces an effective hybrid optimization algorithm, namely Particle Swarm Sine
Cosine Algorithm (PSSCA) for numerical function optimization and automating optimum …

Metaheuristic algorithms are one of the methods used to solve optimization problems and
find global or close to optimal solutions at a reasonable computational cost. As with other …

Inspired by biological systems, swarm intelligence algorithms are widely used to solve
multimodal optimization problems. In this study, we consider the hybridization problem of an …

Quantum computers promise to revolutionize the world of computing thanks to some
features of quantum mechanics that can enable massive parallelism in computation. This …