Multi-swarm Particle Swarm Optimization Algorithm Research Articles

Hybrid multi‐swarm particle swarm optimisation based multi‐objective reactive power dispatch

Most of the real-world optimisation problems are subject to different types of constraints and are known as constrained optimisation problems. Reactive power dispatch (RPD) in electrical power system is also a non-linear, multi-objective or a single objective constrained optimisation problem. In this study, hybrid multi-swarm particle swarm optimisation (HMPSO) algorithm has been proposed to solve the RPD problem. HMPSO is one of the recently proposed population based search algorithm, in which the existing swarm is partitioned into several sub-swarms. Particle swarm optimisation is applied as the search engine for each sub-swarm. In addition, to explore more promising regions of the search space, differential evolution (DE) algorithm is implemented to improve the personal best of each particle. The RPD problem is formulated as non-linear, constrained multi-objective optimisation problem with equality and inequality constraints for minimisation of power losses and improvement of voltage profile simultaneously. To find the Pareto optimal set for RPD problem, weighted sum method has been applied. Afterwards, for finding the preferred solution out of the Pareto-optimal set, fuzzy membership function has been used. Effectiveness of the HMPSO algorithm has been verified on the standard IEEE 30-bus and a practical 75-bus Indian systems.

A MULTISWARM COMPETITIVE PARTICLE SWARM ALGORITHM FOR OPTIMIZATION CONTROL OF AN ETHYLENE CRACKING FURNACE

A fuzzy C-means (FCM) multiswarm competitive particle swarm optimization (FCMCPSO) algorithm is proposed, in which FCM clustering is used to divide swarms adaptively into different clusters. The large-scale swarms are according to the standard particle swarm optimization (PSO) algorithm, whereas the small-scale swarms search randomly in the neighborhood of the optimal solution to increase the probability of jumping out of the local optimization point. Within every cluster, the adaptive value gained by competitive learning is respectively found and arranged in order. Swarms of small adaptive value were integrated with the neighboring swarms of large adaptive value to search the optimal solution competitively by the swarms. The algorithm's validity was tested by benchmark functions and compared with other PSO algorithms. Furthermore, an integrated FCMCPSO-radial basis function neural network was studied for nonlinear system modeling and intelligent optimization control of cracking depth of an ethylene cracking furnace application in a chemical process.

Intelligent damage identification method for large structures based on strain modal parameters

Early damage detection not only improves the safety and reliability of structures but also reduces maintenance cost. However, damage detection is difficult to implement in large structures under ambient excitation because of the limitation of sensors, the uncertainty of ambient excitation, and the global properties of modal frequencies and displacement modes. This paper proposes a new damage detection method that employs the real encoding multi-swarm particle swarm optimization algorithm and fitness functions evolved from strain modes to find the optimal match between measured and simulated modal parameters and to determine the actual condition of structures. The proposed method requires low-frequency modes and incomplete modes and does not require mass normalization of parameters, thus making the method suitable for nondestructive dynamic damage detection of large structures under ambient excitation. Taking a concrete guide wall structure as an example, this paper studied the global searching performance and the sensitivity of the proposed method. The efficiency of the proposed method was analyzed by using different noise levels and sensor numbers. Results show that the proposed method is effective and can be applied in many types of large structures.

Optimal multiaxial sensor placement for modal identification of large structures

SUMMARY Research on optimal sensor placement has become a very important topic because of the need to obtain effective testing results with limited testing resources in modal identification and structural health monitoring. An integer-encoding multi-swarm particle swarm optimisation (IMPSO) algorithm is proposed to place multiaxial sensors optimally on large structures for modal identification. The concepts of grade evaluation and migration strategy and the mutation operators of genetic algorithm are introduced into the integer-encoding particle swarm optimisation algorithm. Three different fitness functions for optimal multiaxial sensor placement (OMSP) are investigated. The second fitness function considers spatial correlation based on Moran's I to solve the information redundancy of multiaxial sensor placement, whereas the other two functions evolve from the existing methods for comparison with the second fitness function. The novel algorithm and three fitness functions are further applied to the Laxiwa arch dam for verifications. The results show that the proposed IMPSO outperforms two existing algorithms in its global optimisation capability. The results also prove that the second fitness function has advantages in sensor distribution and ensuring the well-conditioned information matrix and observability of multidimensional modal shapes. The multiaxial sensor placement scheme determined by the proposed method is applied to the modal test of the Laxiwa arch dam under simulative ambient excitation. The results show that the scheme determined by the second fitness function can identify the frequencies and multidimensional mode shapes accurately, indicating that this method may be used to provide guidance for OMSP in various types of large structures. Copyright © 2013 John Wiley & Sons, Ltd.

A competitive clustering particle swarm optimizer for dynamic optimization problems

Optimization in dynamic optimization problems (DOPs) requires the optimization algorithms not only to locate, but also to continuously track the moving optima. Particle swarm optimization (PSO) is a population-based optimization algorithm, originally developed for static problems. Recently, several researchers have proposed variants of PSO for optimization in DOPs. This paper presents a novel multi-swarm PSO algorithm, namely competitive clustering PSO (CCPSO), designed specially for DOPs. Employing a multi-stage clustering procedure, CCPSO splits the particles of the main swarm over a number of sub-swarms based on the particles positions and on their objective function values. The algorithm automatically adjusts the number of sub-swarms and the corresponding region of each sub-swarm. In addition to the sub-swarms, there is also a group of free particles that explore the environment to locate new emerging optima or exploit the current optima which are not followed by any sub-swarm. The adaptive search strategy adopted by the sub-swarms improves both the exploitation and tracking characteristics of CCPSO. A set of experiments is conducted to study the behavior of the proposed algorithm in different DOPs and to provide guidelines for setting the algorithm’s parameters in different problems. The results of CCPSO on a variety of moving peaks benchmark (MPB) functions are compared with those of several state-of-the-art PSO algorithms, indicating the efficiency of the proposed model.

A comparative study of equivalent circuit models for Li-ion batteries

This paper presents a comparative study of twelve equivalent circuit models for Li-ion batteries. These twelve models were selected from state-of-the-art lumped models reported in the literature. The test data used is obtained from a battery test system with a climate chamber. The test schedule is designed to measure key cell attributes under highly dynamical excitations. The datasets were collected from two types of Li-ion cells under three different temperatures. The multi-swarm particle swarm optimization algorithm is used to identify the optimal model parameters for the two types of Li-ion cells. The usefulness of these models is then studied through a comprehensive evaluation by examining model complexity, model accuracy, and robustness of the model by applying the model to datasets obtained from other cells of the same chemistry type.

A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem

The Probabilistic Traveling Salesman Problem (PTSP) is a variation of the classic Traveling Salesman Problem (TSP) and one of the most significant stochastic routing problems. In the PTSP, only a subset of potential customers need to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. In this paper, a new hybrid algorithmic nature inspired approach based on Particle Swarm Optimization (PSO), Greedy Randomized Adaptive Search Procedure (GRASP) and Expanding Neighborhood Search (ENS) Strategy is proposed for the solution of the PTSP. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm, the classic PSO and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in 13 out of 20 cases the proposed algorithm gives a new best solution.