Metaheuristic optimization algorithms (MOAs) are computational randomized search
processes which draw inspiration from physical and biological phenomena, with an …

The increase in energy consumption, environmental pollution issues, and low-carbon
agenda has grown the research area of demand side management (DSM). DSM programs …

Over the past years, many efforts have been accomplished to achieve fast and accurate
meta-heuristic algorithms to optimize a variety of real-world problems. This study presents a …

Abstract The Firefly Algorithm (FA) is a highly efficient population-based optimization
technique developed by mimicking the flashing behavior of fireflies when mating. This article …

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 a new optimization algorithm based on Newton's law of cooling, which
will be called Thermal Exchange Optimization algorithm. Newton's law of cooling states that …

Energy efficient clustering and routing are two well known optimization problems which have
been studied widely to extend lifetime of wireless sensor networks (WSNs). This paper …

This paper presents a novel metaheuristic algorithm called queuing search (QS), which is
inspired from human activities in queuing. Some common phenomena are considered in …

Firefly algorithm (FA) is a powerful and efficient meta-heuristic algorithm which has shown
effective performance in the recent literature when applied to solving engineering …

Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and
inspiration from the echolocation behavior of bats. One of the issues in the standard bat …