Particle swarm optimization (PSO) is a new swarm intelligent optimization technique. Although it maintains a fast convergent speed, it is still easy trapped into a local optimum when dealing with high-dimensional nume...
详细信息
Particle swarm optimization (PSO) is a new swarm intelligent optimization technique. Although it maintains a fast convergent speed, it is still easy trapped into a local optimum when dealing with high-dimensional numerical problems. To overcome this shortcoming, in this paper, a new variant of PSO is designed hybrid with a dynamic population strategy and crossover operator. Simulation results show this new variant is superior to two other previous modifications in high-dimensional multimodel benchmarks.
The particle swarm optimization (PSO) is a stochastic optimization algorithm imitating animal behavior, which shows a bad performance when optimizing the multimodal and high dimensional functions. Each particle uses o...
详细信息
The particle swarm optimization (PSO) is a stochastic optimization algorithm imitating animal behavior, which shows a bad performance when optimizing the multimodal and high dimensional functions. Each particle uses own experience and other’s to make decision, it is easy to trap into premature convergence, but group decision making with all the individuals to make decisions uses various experiences and viewpoints to get better plan for avoiding conformity. A new formal particle swarm optimization is advanced basing on group decision(GDPSO) it takes each particle as an individual decision-maker and uses the basic information of particle such as the position of individual history and fitness value to decide a new position, then using the position replaces the global best position(pgj),So the space of searching is expanded and the population diversity is increased through the new improved algorithm, it can improve the convergence speed and the capacity of global searching, the premature convergence is avoided to some degree.
Alignment particle swarm optimization (APSO) is a novel variant of particle swarm optimization aiming to improve the population diversity. The topology structure of APSO is gbest model. Since the small-world model is ...
详细信息
Alignment particle swarm optimization (APSO) is a novel variant of particle swarm optimization aiming to improve the population diversity. The topology structure of APSO is gbest model. Since the small-world model is more suit for the natural animal communication network, in this paper, it is incorporated into the methodology of APSO to further improve the performance. Simulation results show this strategy may provide well balance between exploration and exploitation capabilities.
Cognitive learning factor is an important parameter in particle swarm optimization algorithm(PSO). Although many selection strategies have been proposed, there is still much work need to do. Inspired by the black stor...
详细信息
Cognitive learning factor is an important parameter in particle swarm optimization algorithm(PSO). Although many selection strategies have been proposed, there is still much work need to do. Inspired by the black stork foraging process, this paper designs a new cognitive selection strategy, in which the whole swarm is divided into adult and infant particle, and each kind particle has its special choice. Simulation results show this new strategy is superior to other two previous modifications.
Premature convergence is a major problem of Particle Swarm Optimization (PSO).Although many strategies have been proposed, there is still some work needed to do in high-dimensional cases. To overcome this shortcoming,...
详细信息
Premature convergence is a major problem of Particle Swarm Optimization (PSO).Although many strategies have been proposed, there is still some work needed to do in high-dimensional cases. To overcome this shortcoming, a diffused velocity update equation is designed aiming to improve the population diversity with a probability threshold. Simulation results show the performance of this new variant is superior to other two previous modifications when dealing with multi-modal high-dimensional benchmark functions.
Artificial plant optimization algorithm is proposed to solve constrained optimization problems in this paper. In APOA, a shrinkage coefficient is introduce to ensure that all dimensions of a branch are within lower an...
详细信息
Artificial bee colony (ABC) algorithm is a new global stochastic optimization algorithm based on the particular intelligent behavior of honeybee swarms, in which there exists many issues to be improved and solved. Whe...
详细信息
Artificial bee colony (ABC) algorithm is a new global stochastic optimization algorithm based on the particular intelligent behavior of honeybee swarms, in which there exists many issues to be improved and solved. When onlooker bees exploit in ABC algorithm, they choose food source depending on the strategy of proportional selection that can result in the premature of the evolutionary process. In this paper, in order to improve the population diversity and avoid the premature, several selection strategies, such as disruptive selection strategy, tournament selection strategy and rank selection strategy, are compared and analyzed through simulation, and the results show that the modified algorithm outperforms the basic ABC algorithm.
In this paper, the Lyapunov stability theory is employed to analyze the stability of standard version of particle swarm optimization, and a random inertia weight selection strategy is obtained from the analyzed result...
详细信息
In this paper, the Lyapunov stability theory is employed to analyze the stability of standard version of particle swarm optimization, and a random inertia weight selection strategy is obtained from the analyzed results. Simulation results show this random strategy may provide an efficient performance.
Group Search Optimiser (GSO) is a new swarm intelligence optimiser algorithm inspired by animal social behaviours. In this paper, we proposed two variants of GSO to improve its search capability, and applied them to s...
详细信息
暂无评论