Comparative analysis of low discrepancy sequence-based initialization approaches using population-based algorithms for solving the global optimization problems
Metaheuristic algorithms have been widely used to solve diverse kinds of optimization
problems. For an optimization problem, population initialization plays a significant role in …
problems. For an optimization problem, population initialization plays a significant role in …
Studying the impact of initialization for population-based algorithms with low-discrepancy sequences
To solve different kinds of optimization challenges, meta-heuristic algorithms have been
extensively used. Population initialization plays a prominent role in meta-heuristic …
extensively used. Population initialization plays a prominent role in meta-heuristic …
DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization
MH Nadimi-Shahraki, H Zamani - Expert Systems with Applications, 2022 - Elsevier
DE algorithms have outstanding performance in solving complex problems. However, they
also have highlighted the need for an effective approach to alleviating the risk of premature …
also have highlighted the need for an effective approach to alleviating the risk of premature …
Initialisation approaches for population-based metaheuristic algorithms: a comprehensive review
JO Agushaka, AE Ezugwu - Applied Sciences, 2022 - mdpi.com
A situation where the set of initial solutions lies near the position of the true optimality (most
favourable or desirable solution) by chance can increase the probability of finding the true …
favourable or desirable solution) by chance can increase the probability of finding the true …
Three-learning strategy particle swarm algorithm for global optimization problems
X Zhang, Q Lin - Information Sciences, 2022 - Elsevier
Abstract Social Learning Particle Swarm Optimization (SL-PSO) greatly improves the
optimization performance of PSO. In solving complex optimization problems, however, it still …
optimization performance of PSO. In solving complex optimization problems, however, it still …
A new initialization approach in particle swarm optimization for global optimization problems
Particle swarm optimization (PSO) algorithm is a population‐based intelligent stochastic
search technique used to search for food with the intrinsic manner of bee swarming. PSO is …
search technique used to search for food with the intrinsic manner of bee swarming. PSO is …
Modified Teaching–Learning-Based Optimization algorithm for global numerical optimization—A comparative study
SC Satapathy, A Naik - Swarm and Evolutionary Computation, 2014 - Elsevier
Abstract Teaching–Learning-Based Optimization (TLBO) is recently being used as a new,
reliable, accurate and robust optimization technique for global optimization over continuous …
reliable, accurate and robust optimization technique for global optimization over continuous …
Improved global-best-guided particle swarm optimization with learning operation for global optimization problems
In this paper, an improved global-best-guided particle swarm optimization with learning
operation (IGPSO) is proposed for solving global optimization problems. The particle …
operation (IGPSO) is proposed for solving global optimization problems. The particle …
Particle swarm optimization with probability sequence for global optimization
Particle Swarm Optimization (PSO) has been frequently employed to solve diversified
optimization problems. Choosing initial placement for population plays an important role in …
optimization problems. Choosing initial placement for population plays an important role in …
A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization
This article presented a parallel metaheuristic algorithm based on the Particle Swarm
Optimization (PSO) to solve global optimization problems. In recent years, many …
Optimization (PSO) to solve global optimization problems. In recent years, many …