For this reason, there is a trend among researchers to improve existing swarm-based algorithms through different evolutionary techniques and to create new population-based methods that can accurately explore the feature space. The recently proposed Moth swarm algorithm (MSA) inspired by the orientation...
(21) and (22) before entering the succeeding generation swarm. Step 8: After achieving the predefined maximum iterations, the OCSO algorithm is completed. Otherwise, the operation is repeated beginning with step 4. Step 9: After reaching the optimal settings, the load flow is performed using ...
This paper proposes an improved kinetic-molecular theory optimization algorithm (OKMTOA) by analyzing the characteristics of KMTOA cluster behavior and combining the opposition-based learning strategy with varying accelerated motion in physics. The algorithm first applies different opposition-based learning ...
[2] A hybrid scheduler algorithm named FMPSO is introduced that’s derived from the Fuzzy system and Modified Particle Swarm Problem statement Let T = {t1, t2, …, tA} be a tuple of A tasks that should be implemented and V = {v1, v2, …, vN} be a tuple of N VMs (virtual mac...
Moth flame optimization (MFO) algorithm proves to be an excellent choice for numerical optimization. However, for some complex objectives, MFO may get trapped in local optima or suffer from premature convergence. In order to overcome these issues, an improved MFO-based algori...
The robustness of nSCA was rigorously evaluated against leading-edge methods such as the genetic algorithm (GA), particle swarm optimization, moth-flame optimization, ant lion optimization, and multi-verse optimizer, as well as the foundational SCA. This evaluation included benchmarks set by both ...
Feature extraction,Sociology,Statistics,Optimization,Benchmark testing,Particle swarm optimization,ConvergenceIn this paper, an improvement for the Moth-flame Optimization (MFO) algorithm is proposed based on Opposition-Based Learning (OBL), that enhances the exploration of the search space through ...
A meticulous evaluation of nSCA performance has been carried out in comparison with state-of-the-art optimization algorithms, including multi-verse optimizer (MVO), salp swarm algorithm (SSA), moth-flame optimization (MFO), grasshopper optimization algorithm (GOA), and whale o...
[29], earthworm optimization algorithm (EWA) [30], grey wolf optimizer (GWO) [31], [32], firefly algorithm (FA) [33], [34], [35], dragonfly algorithm (DA) [36], harmony search (HS) [37], [38], [39], [40], [41], bird swarm algorithm (BSO) [42], moth-flame ...
Genetic algorithm [5] and differential Evolution [6] can be given as an example to this group. Swarm-based methods are inspired by the swarm intelligence of the animals. Algorithms such as particle s warm optimization [7], bat optimization [8], and cuckoo search optimization [9] are among...