Showing papers on "Particle swarm optimization published in 2015"

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

A Competitive Swarm Optimizer for Large Scale Optimization

Abstract: In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is conceptually very different. In the proposed CSO, neither the personal best position of each particle nor the global best position (or neighborhood best positions) is involved in updating the particles. Instead, a pairwise competition mechanism is introduced, where the particle that loses the competition will update its position by learning from the winner. To understand the search behavior of the proposed CSO, a theoretical proof of convergence is provided, together with empirical analysis of its exploration and exploitation abilities showing that the proposed CSO achieves a good balance between exploration and exploitation. Despite its algorithmic simplicity, our empirical results demonstrate that the proposed CSO exhibits a better overall performance than five state-of-the-art metaheuristic algorithms on a set of widely used large scale optimization problems and is able to effectively solve problems of dimensionality up to 5000.

How effective is the Grey Wolf optimizer in training multi-layer perceptrons

Abstract: This paper employs the recently proposed Grey Wolf Optimizer (GWO) for training Multi-Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification and three function-approximation datasets are utilized to benchmark the performance of the proposed method. For verification, the results are compared with some of the most well-known evolutionary trainers: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolution Strategy (ES), and Population-based Incremental Learning (PBIL). The statistical results prove the GWO algorithm is able to provide very competitive results in terms of improved local optima avoidance. The results also demonstrate a high level of accuracy in classification and approximation of the proposed trainer.

Rolling bearing fault diagnosis using an optimization deep belief network

Abstract: The vibration signals measured from a rolling bearing are usually affected by the variable operating conditions and background noise which lead to the diversity and complexity of the vibration signal characteristics, and it is a challenge to effectively identify the rolling bearing faults from such vibration signals with no further fault information. In this paper, a novel optimization deep belief network (DBN) is proposed for rolling bearing fault diagnosis. Stochastic gradient descent is used to efficiently fine-tune all the connection weights after the pre-training of restricted Boltzmann machines (RBMs) based on the energy functions, and the classification accuracy of the DBN is improved. Particle swarm is further used to decide the optimal structure of the trained DBN, and the optimization DBN is designed. The proposed method is applied to analyze the simulation signal and experimental signal of a rolling bearing. The results confirm that the proposed method is more accurate and robust than other intelligent methods.

Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization

Abstract: A new feature selection approach that is based on the integration of a genetic algorithm and particle swarm optimization is proposed. The overall accuracy of a support vector machine classifier on validation samples is used as a fitness value. The new approach is carried out on the well-known Indian Pines hyperspectral data set. Results confirm that the new approach is able to automatically select the most informative features in terms of classification accuracy within an acceptable CPU processing time without requiring the number of desired features to be set a priori by users. Furthermore, the usefulness of the proposed method is also tested for road detection. Results confirm that the proposed method is capable of discriminating between road and background pixels and performs better than the other approaches used for comparison in terms of performance metrics.

Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation

Abstract: This paper presents a comprehensive learning particle swarm optimization algorithm with enhanced exploration and exploitation, named as “heterogeneous comprehensive learning particle swarm optimization” (HCLPSO). In this algorithm, the swarm population is divided into two subpopulations. Each subpopulation is assigned to focus solely on either exploration or exploitation. Comprehensive learning (CL) strategy is used to generate the exemplars for both subpopulations. In the exploration-subpopulation, the exemplars are generated by using personal best experiences of the particles in the exploration-subpopulation itself. In the exploitation-subpopulation, the personal best experiences of the entire swarm population are used to generate the exemplars. As the exploration-subpopulation does not learn from any particles in the exploitation-subpopulation, the diversity in the exploration-subpopulation can be retained even if the exploitation-subpopulation converges prematurely. The heterogeneous comprehensive learning particle swarm optimization algorithm is tested on shifted and rotated benchmark problems and compared with other recent particle swarm optimization algorithms to demonstrate superior performance of the proposed algorithm over other particle swarm optimization variants.

A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem

Abstract: The Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete optimization problems. In this study, a new hybrid method is proposed to optimize parameters that affect performance of the ACO algorithm using Particle Swarm Optimization (PSO). In addition, 3-Opt heuristic method is added to proposed method in order to improve local solutions. The PSO algorithm is used for detecting optimum values of parameters α and β which are used for city selection operations in the ACO algorithm and determines significance of inter-city pheromone and distances. The 3-Opt algorithm is used for the purpose of improving city selection operations, which could not be improved due to falling in local minimums by the ACO algorithm. The performance of proposed hybrid method is investigated on ten different benchmark problems taken from literature and it is compared to the performance of some well-known algorithms. Experimental results show that the performance of proposed method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness.

Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions

Abstract: A modified ABC algorithm based fast satellite image segmentation has been presented.ABC, PSO and GA methods are compared with this proposed method.The experimental results demonstrate better performance of MABC based technique.The proposed MABC based approach is much faster (CPU time is less).The validity of the proposed technique is reported both qualitatively and quantitatively. In this paper, a modified artificial bee colony (MABC) algorithm based satellite image segmentation using different objective function has been presented to find the optimal multilevel thresholds. Three different methods are compared with this proposed method such as ABC, particle swarm optimization (PSO) and genetic algorithm (GA) using Kapur's, Otsu and Tsallis objective function for optimal multilevel thresholding. The experimental results demonstrate that the proposed MABC algorithm based segmentation can efficiently and accurately search multilevel thresholds, which are very close to optimal ones examined by the exhaustive search method. In MABC algorithm, an improved solution search equation is used which is based on the bee's search only around the best solution of previous iteration to improve exploitation. In addition, to improve global convergence when generating initial population, both chaotic system and opposition-based learning method are employed. Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Abstract: An artificial bee colony based MPPT under partially shaded conditions is proposed.Photovoltaic systems are considered.A co-simulation methodology combining Simulink and Pspice has been adopted.Excellent efficiency and tracking performance compared to the PSO-based MPPT.The effectiveness of the proposed method has been confirmed experimentally. Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink? and Cadence/Pspice?, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.

Economic Allocation for Energy Storage System Considering Wind Power Distribution

Abstract: Energy storage systems play a significant role in both distributed power systems and utility power systems Among the many benefits of an energy storage system, the improvement of power system cost and voltage profile can be the salient specifications of storage systems Studies show that improper size and placement of energy storage units leads to undesired power system cost as well as the risk of voltage stability, especially in the case of high renewable energy penetration To solve the problem, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production Furthermore, the probability cost analysis is first put forward in this paper The proposed HMOPSO combines multi-objective particle swarm optimization (MOPSO) algorithm with elitist nondominated sorting genetic algorithm (NSGA-II) and probabilistic load flow technique It also incorporates a five-point estimation method (5PEM) for discretizing wind power distribution The IEEE 30-bus system is adopted to perform case studies The simulation results for each case clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method

Enhanced leader PSO (ELPSO)

Abstract: A novel optimisation algorithm, named enhanced leader PSO (ELPSO), is introduced.ELPSO mitigates premature convergence problem of conventional PSO.ELPSO is mainly based on a five-staged successive mutation strategy.At each iteration, the successive mutation strategy is applied to swarm leader.The results confirm the outperformance of ELPSO over other compared algorithms. Particle swarm optimisation (PSO) is a well-established optimisation algorithm inspired from flocking behaviour of birds. The big problem in PSO is that it suffers from premature convergence, that is, in complex optimisation problems, it may easily get trapped in local optima. In this paper, a new PSO variant, named as enhanced leader PSO (ELPSO), is proposed for mitigating premature convergence problem. ELPSO is mainly based on a five-staged successive mutation strategy which is applied to swarm leader at each iteration. The experimental results confirm that in all terms of accuracy, scalability and convergence rate, ELPSO performs well.

TSA: Tree-seed algorithm for continuous optimization

Abstract: This paper presents a new intelligent optimizer based on the relation between trees and their seeds for continuous optimization. The new method is in the field of heuristic and population-based search. The location of trees and seeds on n-dimensional search space corresponds with the possible solution of an optimization problem. One or more seeds are produced from the trees and the better seed locations are replaced with the locations of trees. While the new locations for seeds are produced, either the best solution or another tree location is considered with the tree location. This consideration is performed by using a control parameter named as search tendency (ST), and this process is executed for a pre-defined number of iterations. These mechanisms provide to balance exploitation and exploration capabilities of the proposed approach. In the experimental studies, the effects of control parameters on the performance of the method are firstly examined on 5 well-known basic numeric functions. The performance of the proposed method is also investigated on the 24 benchmark functions with 2, 3, 4, 5 dimensions and multilevel thresholding problems. The obtained results are also compared with the results of state-of-art methods such as artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), harmony search (HS) algorithm, firefly algorithm (FA) and the bat algorithm (BA). Experimental results show that the proposed method named as TSA is better than the state-of-art methods in most cases on numeric function optimization and is an alternative optimization method for solving multilevel thresholding problem.

Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate System

Abstract: Managing convergence and diversity is essential in the design of multiobjective particle swarm optimization (MOPSO) in search of an accurate and well distributed approximation of the true Pareto-optimal front. Largely due to its fast convergence, particle swarm optimization incurs a rapid loss of diversity during the evolutionary process. Many mechanisms have been proposed in existing MOPSOs in terms of leader selection, archive maintenance, and perturbation to tackle this deficiency. However, few MOPSOs are designed to dynamically adjust the balance in exploration and exploitation according to the feedback information detected from the evolutionary environment. In this paper, a novel method, named parallel cell coordinate system (PCCS), is proposed to assess the evolutionary environment including density, rank, and diversity indicators based on the measurements of parallel cell distance, potential, and distribution entropy, respectively. Based on PCCS, strategies proposed for selecting global best and personal best, maintaining archive, adjusting flight parameters, and perturbing stagnation are integrated into a self-adaptive MOPSO (pccsAMOPSO). The comparative experimental results show that the proposed pccsAMOPSO outperforms the other eight state-of-the-art competitors on ZDT and DTLZ test suites in terms of the chosen performance metrics. An additional experiment for density estimation in MOPSO illustrates that the performance of PCCS is superior to that of adaptive grid and crowding distance in terms of convergence and diversity.

A new modification approach on bat algorithm for solving optimization problems

Abstract: Optimization can be defined as an effort of generating solutions to a problem under bounded circumstances. Optimization methods have arisen from a desire to utilize existing resources in the best possible way. An important class of optimization methods is heuristic algorithms. Heuristic algorithms have generally been proposed by inspiration from the nature. For instance, Particle Swarm Optimization has been inspired by social behavior patterns of fish schooling or bird flocking. Bat algorithm is a heuristic algorithm proposed by Yang in 2010 and has been inspired by a property, named as echolocation, which guides the bats' movements during their flight and hunting even in complete darkness. In this work, local and global search characteristics of bat algorithm have been enhanced through three different methods. To validate the performance of the Enhanced Bat Algorithm (EBA), standard test functions and constrained real-world problems have been employed. The results obtained by these test sets have proven EBA superior to the standard one. Furthermore, the method proposed in this study is compared with recently published studies in the literature on real-world problems and it is proven that this method is more effective than the studies belonging to other literature on this sort of problems.