This paper comprehensively analyzes the Manta Ray Foraging Optimization (MRFO)
algorithm and its integration into diverse academic fields. Introduced in 2020, the MRFO …

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the
survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get …

The supply-demand-based optimization (SDO) is among the recent stochastic approaches
that have proven its capability in solving challenging engineering tasks. Owing to the non …

This paper proposes a new Multi-Objective Mantis Search Algorithm (MOMSA) to handle
complex optimization problems, including real-world engineering optimization problems …

In this paper, the multi-objective AC-DC optimal power flow (MO/AC-DC OPF) problem in the
presence of renewable energy sources (RESs), flexible AC transmission system (FACTS) …

This paper proposes a multi-objective teaching–learning studying-based algorithm
(MTLSBA) to handle different objective frameworks for solving the large-scale Combined …

This paper introduces a multi-objective variant of the marine predators algorithm (MPA)
called the multi-objective improved marine predators algorithm (MOIMPA), which …

Abstract Introduction Big data optimization (Big-Opt) problems present unique challenges in
effectively managing and optimizing the analytical properties inherent in large-scale …

The Teaching Learning-Based Algorithm (TLBA) is a powerful and effective optimization
approach. TLBA mimics the teaching-learning process in a classroom, where TLBA's …

Losses minimization in distribution networks (DNs) is a critical concern for power utilities
worldwide, especially in both mature and develo** power systems. The growing …