Investigating of Hybrid Meta-Heuristics to Solve the Large-Scale Multi-Source Weber Problems and Performance Measuring of them with Statistical Tests

2013 ◽  
pp. 171-197
Author(s):  
Abdolsalam Ghaderi
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Large Scale ◽  
Statistical Tests ◽  
Hybrid Algorithms ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Local Optima ◽  
Location Allocation ◽  
Large Scale Problems

The location–allocation problems are a class of complicated optimization problems that requires finding sites for m facilities and to simultaneously allocate n customers to those facilities to minimize the total transportation costs. Indeed, these problems, belonging to the class NP-hard, have a lot of local optima solutions. In this chapter, three hybrid meta-heuristics: genetic algorithm, variable neighborhood search and particle swarm optimization, and a hybrid local search approach. These are investigated to solve the uncapacitated continuous location-allocation problem (multi-source Weber problem). In this regard, alternate location allocation and exchange heuristics are used to find the local optima of the problem within the framework of hybrid algorithms. In addition, some large-scale problems are employed to measure the effectiveness and efficiency of hybrid algorithms. Obtained results from these heuristics are compared with local search methods and with each other. The experimental results show that the hybrid meta-heuristics produce much better solutions to solve large-scale problems. Moreover, the results of two non-parametric statistical tests detected a significant difference in hybrid algorithms such that the hybrid variable neighborhood search and particle swarm optimization algorithm outperform the others.

scholarly journals Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm

2020 ◽  
Vol 10 (9) ◽  
pp. 3126
Author(s):  
Desheng Lyu ◽  
Bei Wang ◽  
Weizhe Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Community Structure ◽  
Local Search ◽  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Particle Swarm ◽  
Search Performance ◽  
Swarm Optimization

With the development of network technology and the continuous advancement of society, the combination of various industries and the Internet has produced many large-scale complex networks. A common feature of complex networks is the community structure, which divides the network into clusters with tight internal connections and loose external connections. The community structure reveals the important structure and topological characteristics of the network. The detection of the community structure plays an important role in social network analysis and information recommendation. Therefore, based on the relevant theory of complex networks, this paper introduces several common community detection algorithms, analyzes the principles of particle swarm optimization (PSO) and genetic algorithm and proposes a particle swarm-genetic algorithm based on the hybrid algorithm strategy. According to the test function, the single and the proposed algorithm are tested, respectively. The results show that the algorithm can maintain the good local search performance of the particle swarm optimization algorithm and also utilizes the good global search ability of the genetic algorithm (GA) and has good algorithm performance. Experiments on each community detection algorithm on real network and artificially generated network data sets show that the particle swarm-genetic algorithm has better efficiency in large-scale complex real networks or artificially generated networks.

scholarly journals Optimization of water distribution networks using a modified particle swarm optimization algorithm

2017 ◽  
Vol 18 (2) ◽  
pp. 660-678 ◽  
Author(s):  
Douglas F. Surco ◽  
Thelma P. B. Vecchi ◽  
Mauro A. S. S. Ravagnani
Keyword(s):  
Particle Swarm Optimization ◽  
Water Distribution ◽  
Large Scale ◽  
Particle Swarm ◽  
Minimum Cost ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Optima

Abstract In the present work, a model is presented for the optimization of water distribution networks (WDN). The developed model can be used to verify node pressures, head losses, and fluid flow rate and velocity in each pipe. The algorithm is based on particle swarm optimization (PSO), considering real and discrete variables and avoiding premature convergence to local optima using objective function penalization. The model yields the minimum cost of the network, the node pressures and the velocities in the pipes. The pressures and velocities are calculated using the hydraulic simulator Epanet. Some benchmark problems are used to test the applicability of the developed model, considering WDN for small-, medium-, and large-scale problems. Obtained results are consistent with those found in the literature.

scholarly journals Heterogeneous Cooperative Bare-Bones Particle Swarm Optimization with Jump for High-Dimensional Problems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1539
Author(s):  
Joonwoo Lee ◽  
Won Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Particle Swarm ◽  
Optimization Method ◽  
High Dimensional ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Optima ◽  
One Step ◽  
Bare Bones

This paper proposes a novel Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for solving high-dimensional problems. BBPSO is a variant of Particle Swarm Optimization (PSO) and is based on a Gaussian distribution. The BBPSO algorithm does not consider the selection of controllable parameters for PSO and is a simple but powerful optimization method. This algorithm, however, is vulnerable to high-dimensional problems, i.e., it easily becomes stuck at local optima and is subject to the “two steps forward, one step backward” phenomenon. This study improves its performance for high-dimensional problems by combining heterogeneous cooperation based on the exchange of information between particles to overcome the “two steps forward, one step backward” phenomenon and a jumping strategy to avoid local optima. The CEC 2010 Special Session on Large-Scale Global Optimization (LSGO) identified 20 benchmark problems that provide convenience and flexibility for comparing various optimization algorithms specifically designed for LSGO. Simulations are performed using these benchmark problems to verify the performance of the proposed optimizer by comparing the results of other variants of the PSO algorithm.

scholarly journals Recent Advances in Particle Swarm Optimization for Large Scale Problems

2018 ◽  
Vol 1 (1) ◽  
pp. 22 ◽  
Author(s):  
Danping Yan ◽  
Yongzhong Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Rapid Development ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Large Scale Problems ◽  
Design Variables ◽  
Huge Impact

Accompanied by the advent of current big data ages, the scales of real world optimization problems with many decisive design variables are becoming much larger. Up to date, how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of bio-inspired computation. So addressing these complex large scale problems to produce truly useful results is one of the presently hottest topics. As a branch of the swarm intelligence based algorithms, particle swarm optimization (PSO) for coping with large scale problems and its expansively diverse applications have been in rapid development over the last decade years. This review paper mainly presents its recent achievements and trends, and also highlights the existing unsolved challenging problems and key issues with a huge impact in order to encourage further more research in both large scale PSO theories and their applications in the forthcoming years.

Skip Neighborhood Hybrid Particle Swarm Optimization Algorithm

2011 ◽  
Vol 311-313 ◽  
pp. 1863-1868
Author(s):  
Jian Jun Li ◽  
Bin Yu ◽  
Wu Ping Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Particle Swarm ◽  
Global Search ◽  
Local Optimization ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Local Neighborhood ◽  
Hybrid Particle Swarm Optimization ◽  
Speed Accuracy

Traditional Particle Swarm Optimization (PSO) uses single search strategy and is difficult to balance the global search with local search, and easy to fall into local optimization, a new algorithm which integrates global search with local neighborhood search is presented. The algorithm performs the global search in parallel with the local search by the feedback of the global optimal particle and the information interaction of local neighborhood. Meanwhile, with a new neighborhood topology to control the search space, the algorithm can avoid the local optimization successfully. Tested by four classical functions, the new algorithm performs well on optimization speed, accuracy and success rate.

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