The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of...

This paper presented a new particle swarm optimization based on evolutionary game theory (EPSO) for the traveling salesman problem (TSP) to overcome the disadvantages of premature convergence and stagnation phenomenon of traditional particle swarm optimization algorithm (PSO). In addition ,we make a mapping among the three parts discrete particle swarm optimization (DPSO)、 evolutionary game the...

A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

This study examines a variable-length Particle Swarm Algorithm for Social Programming. The Grammatical Swarm algorithm is a form of Social Programming as it uses Particle Swarm Optimisation, a social swarm algorithm, for the automatic generation of programs. This study extends earlier work on a fixed-length incarnation of Grammatical Swarm, where each individual particle represents choices of p...

The variable ordering of constraint satisfaction problems affect the performance of search algorithms in CSPs. Dynamic Variable Ordering (DVO) has more advantage in improving the performance of search algorithms than static variable ordering. It is a newly developed method recent years that using particle swarm algorithm to solve binary constraint satisfaction problems, which is a global stocha...

Based on introducing two optimization algorithms, group search optimization (GSO) algorithm and particle swarm optimization (PSO) algorithm, a new hybrid optimization algorithm which named particle swarm-group search optimization (PS-GSO) algorithm is presented and its application to optimal structural design is analyzed. The PS-GSO is used to investigate the spatial truss structures with discr...

Heart chaotic system and the ability of particle swarm optimization (PSO) method motivated us to benefit the method of chaotic particle swarm optimization (CPSO) to synchronize the heart three-oscillator model. It can be a suitable algorithm for strengthening the controller in presence of unknown parameters. In this paper we apply adaptive control (AC) on heart delay model, also examine the sys...

this paper proposes a novel hybrid algorithm namely apso-bfo which combines merits of bacterial foraging optimization (bfo) algorithm and adaptive particle swarm optimization (apso) algorithm to determine the optimal pid parameters for control of nonlinear systems. to balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...

the improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. detailed examination of changes in expression levels of genes can help physicians to have effi cient diagnosing, classification of tumors and cancer’s types as well as effective treatments. finding genes that can classify the group of...

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at t...