A hybrid ABC-SPSO algorithm for continuous function optimization
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In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Two different hybrid algorithms are tested in this work based on the method in which the ABC component is applied to the different particles. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics.Keywords:
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A novel approach for nonlinear system identification is proposed based on adaptive particle swarm optimization in this paper. Particle swarm optimization is demonstrated as efficient global search method for complex surfaces, and in order to quick the convergence speed, an adaptive particle swarm optimization strategy was introduced. The proposed method formulates the nonlinear system identification as an optimization problem in parameter space, and then adaptive particle swarm optimization are used in the optimization process to find the estimation values of the parameters respectively. Application to Wiener model, in which the nonlinear static subsystems and linear dynamic are separated in different order, is studied and compared with other methods and the simulation results show the identification by adaptive particle swarm optimization is very effective and superior accuracy.
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The linear decreasing weight particle swarm optimization algorithm (LDWPSO) is mentioned in the concept of a center particle, and then puts forward center particle swarm optimization algorithm (PSO). The linear decreasing weight particle swarm optimization algorithm, unlike other general center particle, particle velocity center is not clear, and is always placed in the center of the particle swarm. In addition, the neural network training algorithm compared to particle swarm optimization algorithm and the linear decreasing weight particle swarm optimization algorithm, results show that: the performance is better than the linear optimization center particle swarm decreasing weight PSO algorithm. algorithm.
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In this paper a novel approach for nonlinear system identification is proposed based on adaptive particle swarm optimization. Particle swarm optimization is demonstrated as efficient global search method for complex surfaces, and in order to quick the convergence speed, an adaptive particle swarm optimization strategy was introduced. The proposed method formulates the nonlinear system identification as an optimization problem in parameter space, and then adaptive particle swarm optimization are used in the optimization process to find the estimation values of the parameters respectively. Application to Hammerstein model, in which the nonlinear static subsystems and linear dynamic are separated in different order, is studied and compared with other methods and the simulation results show the identification by adaptive particle swarm optimization is very effective and superior accuracy.
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Particle swarm optimization (PSO) is an algorithm widely used to solve optimization problems. Multi-swarm particle swarm optimization (MSPSO) is a form of particle swarm optimization (PSO). The swarm size of the multi-swarm particle swarm optimization (MSPSO) algorithm is an important characteristic of the algorithm. The number of particles and the number of swarms will affect the performance of the algorithm to varying degrees. The algorithm performance of multi-swarm particle swarm optimization algorithm is studied from the perspective of swarm size.
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Swarm intelligence is the collective intelligence of group of independent agents. It can be applied in optimization. So researchers are increasingly interested in swarm Intelligence. The paper describes the swarm Intelligence. Paper discusses type of biological system - social system, more specifically while focusing on the aspects of developments of Particle Swarm Optimization. Definitions of key terms are provided with two neighborhood topologies. Included are brief discussions on the important variations of Particle Swarm Optimization. These variations are to improve the convergence rate of Particle Swarm Optimization.
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Hybrid particle swarm optimization was presented to improve the optimizing efficiency of the particle swarm by changing the optimizing strategy of the global best particle.Aimed at the problem of optimization with a limit on computing time, such as the state prediction of a typical equipment in process industry, hybrid particle swarm optimization took the global best position found by the particle swarm as a special particle, which performed the gradient descending optimization.By adding the individual gradient descending optimization of the global best particle to the optimization iterations, the global search and local search were combined in hybrid particle swarm optimization.The hybridism of this new particle swarm optimization improved the optimizing efficiency of the particle swarm, and reduced the time of optimization computing.In the test of a real application, hybrid particle swarm optimization was applied to the state prediction of the continuous stirred tank reactor (CSTR), which is a typical equipment of the process industry.In the test training of neural network that was used in the prediction of the concentration of the CSTR product, hybrid particle swarm optimization took less optimizing iterations than the traditional particle swarm optimization, and took less optimization computing time, which showed that hybrid particle swarm optimization could reduce the computing time of optimization as the original intent of this research.
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The performance of the particle swarm is mainly influenced by individual particles experience and group experience in the period of evolution for particle swarm optimization. To make full use of the two factors and effectively improve the particle swarm optimization performance, Introduced a novel Two-subpopulation Particle Swarm Optimization, The proportion of individual experience and group experiences is different in each subpopulation swarm. If the proportion of individual experience greater than the group experience, the particle swarm search space abroad, whereas, the proportion of group experience greater than individual experience, the particle swarm search the local area fully. The proposed Two-subpopulation particle swarm optimization combines both advantages, make the search more fully and not easily into the local minimum points. Finally simulations were carried out and the results showed that the proposed Two-subpopulation particle swarm optimization, obviously better than the basic particle swarm algorithm in search precision and stability.
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Artificial bee colony (ABC) algorithm is one of the most popular swarm intelligence based algorithms. ABC has been applied to solve several problems in various fields and also many researchers have attempted to improve ABC's performance by making some modifications. This works aims to model the behaviour of foragers of artificial bee colony more accurately and improve the performance of ABC algorithm in terms of local search ability. Therefore, a new definition is introduced for the onlookers of ABC. The new ABC is tested on a set of benchmark problems.
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