Add to ORKGRecently, an improved Vector Evaluated Particle Swarm Optimisation (VEPSO) algorithm is introduced by redefining the swarm's leader as non-dominated solutions. The improved VEPSO algorithm is named as VEPSOnds. Since a parameter tuning of a heuristic algorithm is normally difficult. Hence, in this paper, three important parameters of the improved VEPSO, which are inertia weight, cognitive constant, and social constant, are analyzed. The results suggest that the inertia weight should gradually degrade from 1.0 to 0.4, and both cognitive and social constants to be random value in between 1.5 and 2.5
Multi-objective optimization can be commonly found in many real world problems. In computational int...
Multi-objective optimisation problem is the problem which contains more than one objective that need...
In particle swarm optimization (PSO), the inertia weight is an important parameter for controlling i...
Recently, an improved Vector Evaluated Particle Swarm Optimization (VE PSO) algorithm has been intro...
The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorp...
In this paper, we introduce a new parameter, called inertia weight, into the original particle swarm...
Copyright © 2014 Kian Sheng Lim et al.This is an open access article distributed under theCreative C...
Abstract. For improving the performance of the Particle Swarm Optimization (PSO), two major strategi...
Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collectiv...
Particle Swarm Optimization (PSO) is a population based stochastic optimization technique inspired b...
Abstract—Many optimisation problems are multi-objective and change dynamically. Many methods use a w...
The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective op...
: Particel Swarm Optimization (PSO) is a form of population evolutionary algorithm introduced in the...
The particle swarm optimization (PSO) algorithm is a stochastic, population-based optimization techn...
Multi-objective optimization can be commonly found in many real world problems. In computational int...
Multi-objective optimization can be commonly found in many real world problems. In computational int...
Multi-objective optimisation problem is the problem which contains more than one objective that need...
In particle swarm optimization (PSO), the inertia weight is an important parameter for controlling i...
Recently, an improved Vector Evaluated Particle Swarm Optimization (VE PSO) algorithm has been intro...
The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorp...
In this paper, we introduce a new parameter, called inertia weight, into the original particle swarm...
Copyright © 2014 Kian Sheng Lim et al.This is an open access article distributed under theCreative C...
Abstract. For improving the performance of the Particle Swarm Optimization (PSO), two major strategi...
Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collectiv...
Particle Swarm Optimization (PSO) is a population based stochastic optimization technique inspired b...
Abstract—Many optimisation problems are multi-objective and change dynamically. Many methods use a w...
The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective op...
: Particel Swarm Optimization (PSO) is a form of population evolutionary algorithm introduced in the...
The particle swarm optimization (PSO) algorithm is a stochastic, population-based optimization techn...
Multi-objective optimization can be commonly found in many real world problems. In computational int...
Multi-objective optimization can be commonly found in many real world problems. In computational int...
Multi-objective optimisation problem is the problem which contains more than one objective that need...
In particle swarm optimization (PSO), the inertia weight is an important parameter for controlling i...