A comprehensive survey on particle swarm optimization algorithm and its applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed
originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used …
originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used …
A review on constraint handling strategies in particle swarm optimisation
AR Jordehi - Neural Computing and Applications, 2015 - Springer
Almost all real-world optimisation problems are constrained. Solving constrained problems
is difficult for optimisation techniques. In this paper, different constraint handling strategies …
is difficult for optimisation techniques. In this paper, different constraint handling strategies …
A constrained multi-swarm particle swarm optimization without velocity for constrained optimization problems
The original particle swarm optimization (PSO) is not able to tackle constrained optimization
problems (COPs) due to the absence of constraint handling techniques. Furthermore, most …
problems (COPs) due to the absence of constraint handling techniques. Furthermore, most …
Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system
W Hu, GG Yen - IEEE Transactions on Evolutionary …, 2013 - ieeexplore.ieee.org
Managing convergence and diversity is essential in the design of multiobjective particle
swarm optimization (MOPSO) in search of an accurate and well distributed approximation of …
swarm optimization (MOPSO) in search of an accurate and well distributed approximation of …
A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts
Group decision making is a type of decision problem in which multiple experts acting
collectively, analyze problems, evaluate alternatives, and select a solution from a collection …
collectively, analyze problems, evaluate alternatives, and select a solution from a collection …
Building consensus in group decision making with an allocation of information granularity
Consensus is defined as a cooperative process in which a group of decision makers
develops and agrees to support a decision in the best interest of the whole. It is a …
develops and agrees to support a decision in the best interest of the whole. It is a …
Two-Stage Multiobjective Evolution Strategy for Constrained Multiobjective Optimization
For the past many years, several constrained multiobjective evolutionary algorithms
(CMOEAs) have been designed for solving constrained multiobjective optimization problems …
(CMOEAs) have been designed for solving constrained multiobjective optimization problems …
Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems
Differential evolution (DE) is a simple but powerful evolutionary algorithm used in multiple
sciences and engineering disciplines to tackle optimization problems. DE has some …
sciences and engineering disciplines to tackle optimization problems. DE has some …
A surrogate-assisted differential evolution for expensive constrained optimization problems involving mixed-integer variables
Abstract Many Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been developed
for expensive constrained optimization problems (ECOPs) with continuous variables …
for expensive constrained optimization problems (ECOPs) with continuous variables …
Kriging-assisted teaching-learning-based optimization (KTLBO) to solve computationally expensive constrained problems
H Dong, P Wang, C Fu, B Song - Information Sciences, 2021 - Elsevier
In this paper, a novel algorithm KTLBO is presented to achieve computationally expensive
constrained optimization. In KTLBO, Kriging is adopted to develop dynamically updated …
constrained optimization. In KTLBO, Kriging is adopted to develop dynamically updated …