Self-adaptive Particle Swarm Optimization Research Articles

Particle Swarm Optimization of a Multi-Coil Transverse Flux Induction Heating System

In this paper the optimization of a transverse flux induction heating device by means of a parameter-free, self-adaptive particle swarm optimizer is presented. The novelty of this work is represented by the fact that concentric multi-coil induction heaters have never been optimized before in their full three-dimensional complexity. The main technical challenge of the problem consists in designing a single configuration capable of heating strips of different widths while satisfying geometrical, electrical and heating process constraints. It will be shown that a system consisting of three coils can successfully heat strips of widths between 600 mm and 800 mm with the required uniformity.

Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects

This paper presents a chaotic self-adaptive particle swarm optimization algorithm (CSAPSO) to solve dynamic economic dispatch problem (DED) with value-point effects. The proposed algorithm takes PSO as the main evolution method. The velocity, a sensitive parameter of PSO, is adjusted dynamically to increase the precision of PSO. To overcome the drawback of premature in PSO, chaotic local search is imported into proposed algorithm. Moreover, a new strategy is proposed to handle the various constraints of DED problem in this paper, the results solved by proposed strategy can satisfy the constraints of DED problem well. Finally, the high feasibility and effectiveness of proposed CSAPSO algorithm is validated by three test systems consisting of 10 and extended 30 generators while compared with the experimental results calculated by the other methods reported in this literature.

Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud

Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum that represents target servers for VM placement. The experimental results of SAPSO was compared with Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) and the results show that SAPSO outperforms the latter for power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.

A hybrid evolutionary algorithm for distribution feeder reconfiguration

This paper presents a new method to reduce the distribution system loss by feeder reconfiguration. This new method combines self-adaptive particle swarm optimization (SAPSO) with shuffled frog-leaping algorithm (SFLA) in an attempt to find the global optimal solutions for the distribution feeder reconfiguration (DFR). In PSO algorithm, appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort. Thus, a self-adaptive framework is proposed to improve the robustness of PSO. In SAPSO the learning factors of PSO coevolve with the particles. SFLA is combined with the SAPSO algorithm to improve its performance. The proposed algorithm is tested on two distribution test networks. The results of simulation show that the proposed algorithm is very powerful and guarantees to obtain the global optimization in minimum time.

A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration

One of the very important way to save the electrical energy in distribution system is network reconfiguration for loss reduction. This paper proposes a new hybrid evolutionary algorithm for solving the distribution feeder reconfiguration (DFR) problem. The proposed hybrid evolutionary algorithm is the combination of SAPSO (self-adaptive particle swarm optimization) and MSFLA (modified shuffled frog leaping algorithm), called SAPSO–MSFLA, which can find optimal configuration of distribution network. In the PSO algorithm, appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort. Therefore, a self-adaptive framework is proposed to improve the robustness of the PSO, also in the modified shuffled frog leaping algorithm (MSFLA) to improve the performance of algorithm a new frog leaping rule is proposed to improve the local exploration of the SFLA. The main idea of integrating SAPSO and MSFLA is to use their advantages and avoid their disadvantages. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization in minimum time.

An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity

To deal with the problem of premature convergence and slow search speed, this paper proposes a novel particle swarm optimization (PSO) called self-adaptive escape PSO, which is guaranteed to converge to the global optimization solution with probability one. Considering that the organisms have the phenomena of escaping from the original cradle when they find the survival density is too high to live, this paper uses a special mutation –escape operator to make particles explore the search space more efficiently. The novel strategy produces a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Experimental simulations show that the proposed method can not only significantly speed up the convergence, but also effectively solve the premature convergence problem .