scholarly journals Improved Barebones Particle Swarm Optimization with Neighborhood Search and Its Application on Ship Design

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jingzheng Yao ◽  
Duanfeng Han
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Ship Design ◽  
Neighborhood Search ◽  
Search Process ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Real World Problem ◽  
Complex Optimization

Barebones particle swarm optimization (BPSO) is a new PSO variant, which has shown a good performance on many optimization problems. However, similar to the standard PSO, BPSO also suffers from premature convergence when solving complex optimization problems. In order to improve the performance of BPSO, this paper proposes a new BPSO variant called BPSO with neighborhood search (NSBPSO) to achieve a tradeoff between exploration and exploitation during the search process. Experiments are conducted on twelve benchmark functions and a real-world problem of ship design. Simulation results demonstrate that our approach outperforms the standard PSO, BPSO, and six other improved PSO algorithms.

scholarly journals Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao Fu ◽  
Wangsheng Liu ◽  
Bin Zhang ◽  
Hua Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Studies ◽  
Particle Swarm ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Complex Optimization

Quantum-behaved particle swarm optimization (QPSO) algorithm is a new PSO variant, which outperforms the original PSO in search ability but has fewer control parameters. However, QPSO as well as PSO still suffers from premature convergence in solving complex optimization problems. The main reason is that new particles in QPSO are generated around the weighted attractors of previous best particles and the global best particle. This may result in attracting too fast. To tackle this problem, this paper proposes a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies are utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning (OBL) is employed for population initialization. Experimental studies are conducted on a set of well-known benchmark functions including multimodal and rotated problems. Computational results show that our approach outperforms some similar QPSO algorithms and five other state-of-the-art PSO variants.

scholarly journals Chunking and cooperation in particle swarm optimization for feature selection

2021 ◽  
Author(s):  
Malek Sarhani ◽  
Stefan Voß
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Cooperative Learning ◽  
Optimization Problems ◽  
Practical Importance ◽  
Particle Swarm ◽  
Large Datasets ◽  
Social Phenomena ◽  
Swarm Optimization ◽  
Complex Optimization

AbstractBio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

2008 ◽  
Vol 12 (3) ◽  
pp. 284-289 ◽  
Author(s):  
Jenn-Long Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Social Learning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Design Parameters ◽  
Search Process ◽  
Swarm Optimization ◽  
Learning Rates

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

A Modified Particle Swarm Optimization Algorithm Based on Proportional Distribution of Particles

2011 ◽  
Vol 181-182 ◽  
pp. 937-942
Author(s):  
Bo Liu ◽  
Hong Xia Pan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Global Optimum ◽  
The Self ◽  
Swarm Optimization ◽  
Local Optima ◽  
Modified Particle Swarm Optimization ◽  
Complex Optimization ◽  
Proportional Distribution

Particle swarm optimization (PSO) is widely used to solve complex optimization problems. However, classical PSO may be trapped in local optima and fails to converge to global optimum. In this paper, the concept of the self particles and the random particles is introduced into classical PSO to keep the particle diversity. All particles are divided into the standard particles, the self particles and the random particles according to special proportion. The feature of the proposed algorithm is analyzed and several testing functions are performed in simulation study. Experimental results show that, the proposed PDPSO algorithm can escape from local minima and significantly enhance the convergence precision.

scholarly journals Artificial God Optimization - A Creation

2020 ◽  
Vol 13 (1) ◽  
pp. 41
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
The World ◽  
Complex Optimization ◽  
Primary Focus

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence, the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field, which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm, which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

A Novel Hybrid Particle Swarm Optimization Algorithm

2013 ◽  
Vol 409-410 ◽  
pp. 1611-1614
Author(s):  
Lei Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Step Length ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Hybrid Particle Swarm Optimization ◽  
Complex Optimization ◽  
Effectiveness And Efficiency

Particle swarm optimization (PSO) is a global algorithm which is inspired by birds flocking and fish schooling. PSO has shown good search ability in many complex optimization problems, but premature convergence is still a main problem. A novel hybrid PSO(NHPSO) was proposed, which employed hybrid strategies, including dynamic step length (DSL) and opposition-based learning (OBL). DSL is helpful to enhance local search ability of PSO, and OBL is beneficial for improving the quality of candidate solutions. In order to verify the performance of NHPSO, we test it on several benchmark functions. The simulation results demonstrate the effectiveness and efficiency of our approach.

scholarly journals Dynamic Neighborhood-Based Particle Swarm Optimization for Multimodal Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xu-Tao Zhang ◽  
Biao Xu ◽  
Wei Zhang ◽  
Jun Zhang ◽  
Xin-fang Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Multimodal Optimization ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Benchmark Instances ◽  
Multimodal Problems

Various black-box optimization problems in real world can be classified as multimodal optimization problems. Neighborhood information plays an important role in improving the performance of an evolutionary algorithm when dealing with such problems. In view of this, we propose a particle swarm optimization algorithm based on dynamic neighborhood to solve the multimodal optimization problem. In this paper, a dynamic ε-neighborhood selection mechanism is first defined to balance the exploration and exploitation of the algorithm. Then, based on the information provided by the neighborhoods, four different particle position updating strategies are designed to further support the algorithm’s exploration and exploitation of the search space. Finally, the proposed algorithm is compared with 7 state-of-the-art multimodal algorithms on 8 benchmark instances. The experimental results reveal that the proposed algorithm is superior to the compared ones and is an effective method to tackle multimodal optimization problems.

scholarly journals Parameter Identification of Anaerobic Wastewater Treatment Bioprocesses Using Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Dorin Sendrescu
Keyword(s):  
Wastewater Treatment ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Wastewater Treatment Process ◽  
Swarm Optimization ◽  
Anaerobic Wastewater Treatment ◽  
Complex Optimization

This paper deals with the offline parameters identification for a class of wastewater treatment bioprocesses using particle swarm optimization (PSO) techniques. Particle swarm optimization is a relatively new heuristic method that has produced promising results for solving complex optimization problems. In this paper one uses some variants of the PSO algorithm for parameter estimation of an anaerobic wastewater treatment process that is a complex biotechnological system. The identification scheme is based on a multimodal numerical optimization problem with high dimension. The performances of the method are analyzed by numerical simulations.

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