Particle Swarm Optimization Combining Diversification and Intensification for Nonlinear Integer Programming Problems

In this research, focusing on nonlinear integer programming problems, we propose an approximate solution method based on particle swarm optimization proposed by Kennedy et al. And we developed a new particle swarm optimization method which is applicable to discrete optimization problems by incoporating a new method for generating initial search points, the rounding of values obtained by the move scheme and the revision of move methods. Furthermore, we showed the efficiency of the proposed particle swarm optimization method by comparing it with an existing method through the application of them into the numerical examples. Moreover we expanded revised particle swarm optimization method for application to nonlinear integer programming problems and showed more effeciency than genetic algorithm. However, variance of the solutions obtained by the PSO method is large and accuracy is not so high. Thus, we consider improvement of accuracy introducing diversification and intensification

A Discrete Geese Swarm Algorithm for Spectrum Assignment of Cognitive Radio

In order to solve spectrum assignment problem, this paper proposes a discrete geese swarm algorithm (DGSA) based on particle swarm optimization and quantum particle swarm optimization, and we evaluate the performance of the DGSA through some classical benchmark functions. The proposed DGSA algorithm applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization. We also use it to solve cognitive radio spectrum assignment problem. The new spectrum allocation method has the ability to search global optimal solution under different network utility functions. Simulation results for cognitive radio system are provided to show that the designed spectrum allocation algorithm is superior to some previous spectrum allocation algorithms

Chaotic particle swarm optimization

Abstract: A new particle swarm optimization (PSO) algorithm with has a chaotic neural network structure, is proposed. The structure is similar to the Hop¯eld neural network with transient chaos, and has an improved ability to search for globally optimal solution and does not su®er from problems of premature convergence. The presented PSO model is discrete-time discrete-state. The bifurcation diagram of a particle shows that it converges to a stable fixed point from a strange attractor, guaranteeing system convergence

Soccer game optimization for continuous and discrete problem

Soccer games optimization is a new metaheuristics method that mimics the soccer player’s movement, wherein each player decides their best positions to dribble the ball towards the goal based on the ball position and other players’ position. This paper discussed the method for continuous and discrete problems based on ‘pair cooperation’ between a player and the ball position. The algorithm is implemented in eight benchmark problems consisting of continuous unconstrained problems, continuous constrained problems and discrete problem. The performance of the algorithm for the continuous unconstrained problems is compared to two meta-heuristic algorithms, the genetic algorithm and the particle swarm optimization. The continuous constrained problems and the discrete problem are compared with the result in the literature. The experimental results show that the algorithm is a potentially powerful optimization procedure that can be applied for various optimization problems

Soccer game optimization for continuous and discrete problems

Soccer games optimization is a new metaheuristics method that mimics the soccer player’s movement, wherein each player decides their best positions to dribble the ball towards the goal based on the ball position and other players’ position. This paper discussed the method for continuous and discrete problems based on ‘pair cooperation’ between a player and the ball position. The algorithm is implemented in eight benchmark problems consisting of continuous unconstrained problems, continuous constrained problems and discrete problem. The performance of the algorithm for the continuous unconstrained problems is compared to two meta-heuristic algorithms, the genetic algorithm and the particle swarm optimization. The continuous constrained problems and the discrete problem are compared with the result in the literature. The experimental results show that the algorithm is a potentially powerful optimization procedure that can be applied for various optimization problems

Soccer game optimization for continuous and discrete problem

Soccer games optimization is a new metaheuristics method that mimics the soccer player’s movement, wherein each player decides their best positions to dribble the ball towards the goal based on the ball position and other players’ position. This paper discussed the method for continuous and discrete problems based on ‘pair cooperation’ between a player and the ball position. The algorithm is implemented in eight benchmark problems consisting of continuous unconstrained problems, continuous constrained problems and discrete problem. The performance of the algorithm for the continuous unconstrained problems is compared to two meta-heuristic algorithms, the genetic algorithm and the particle swarm optimization. The continuous constrained problems and the discrete problem are compared with the result in the literature. The experimental results show that the algorithm is a potentially powerful optimization procedure that can be applied for various optimization problems

Particle Swarm Optimization Combining Diversification and Intensification for Nonlinear Integer Programming Problems

Abstract-In this research, focusing on nonlinear integer programming problems, we propose an approximate solution method based on particle swarm optimization proposed by Kennedy et al. And we developed a new particle swarm optimization method which is applicable to discrete optimization problems by incoporating a new method for generating initial search points, the rounding of values obtained by the move scheme and the revision of move methods. Furthermore, we showed the efficiency of the proposed particle swarm optimization method by comparing it with an existing method through the application of them into the numerical examples. Moreover we expanded revised particle swarm optimization method for application to nonlinear integer programming problems and showed more effeciency than genetic algorithm. However, variance of the solutions obtained by the PSO method is large and accuracy is not so high. Thus, we consider improvement of accuracy introducing diversification and intensification