Constraint Handling in Particle Swarm Optimization

2010 ◽  
Vol 1 (1) ◽  
pp. 42-63 ◽  
Author(s):  
Wen Fung Leong ◽  
Gary G. Yen
Keyword(s):  
Particle Swarm Optimization ◽  
Pareto Front ◽  
Particle Swarm ◽  
Constraint Handling ◽  
Hidden Information ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Local Searches ◽  
Multiobjective Approach ◽  
Fine Tune

In this article, the authors propose a particle swarm optimization (PSO) for constrained optimization. The proposed PSO adopts a multiobjective approach to constraint handling. Procedures to update the feasible and infeasible personal best are designed to encourage finding feasible regions and convergence toward the Pareto front. In addition, the infeasible nondominated solutions are stored in the global best archive to exploit the hidden information for guiding the particles toward feasible regions. Furthermore, the number of feasible personal best in the personal best memory and the scalar constraint violations of personal best and global best are used to adapt the acceleration constants in the PSO flight equations. The purpose is to find more feasible particles and search for better solutions during the process. The mutation procedure is applied to encourage global and fine-tune local searches. The simulation results indicate that the proposed constrained PSO is highly competitive, achieving promising performance.

Constraint Handling in Particle Swarm Optimization

2012 ◽  
pp. 60-80
Author(s):  
Wen Fung Leong ◽  
Gary G. Yen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Constraint Handling ◽  
Hidden Information ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Local Searches ◽  
Multiobjective Approach ◽  
Simulation Results ◽  
Fine Tune

In this article, the authors propose a particle swarm optimization (PSO) for constrained optimization. The proposed PSO adopts a multiobjective approach to constraint handling. Procedures to update the feasible and infeasible personal best are designed to encourage finding feasible regions and convergence toward the Pareto front. In addition, the infeasible nondominated solutions are stored in the global best archive to exploit the hidden information for guiding the particles toward feasible regions. Furthermore, the number of feasible personal best in the personal best memory and the scalar constraint violations of personal best and global best are used to adapt the acceleration constants in the PSO flight equations. The purpose is to find more feasible particles and search for better solutions during the process. The mutation procedure is applied to encourage global and fine-tune local searches. The simulation results indicate that the proposed constrained PSO is highly competitive, achieving promising performance.

scholarly journals Adaptive Multiswarm Comprehensive Learning Particle Swarm Optimization

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 173 ◽  
Author(s):  
Xiang Yu ◽  
Claudio Estevez
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Velocity ◽  
Pareto Front ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Performance ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
The Difference ◽  
Update Strategy

Multiswarm comprehensive learning particle swarm optimization (MSCLPSO) is a multiobjective metaheuristic recently proposed by the authors. MSCLPSO uses multiple swarms of particles and externally stores elitists that are nondominated solutions found so far. MSCLPSO can approximate the true Pareto front in one single run; however, it requires a large number of generations to converge, because each swarm only optimizes the associated objective and does not learn from any search experience outside the swarm. In this paper, we propose an adaptive particle velocity update strategy for MSCLPSO to improve the search efficiency. Based on whether the elitists are indifferent or complex on each dimension, each particle adaptively determines whether to just learn from some particle in the same swarm, or additionally from the difference of some pair of elitists for the velocity update on that dimension, trying to achieve a tradeoff between optimizing the associated objective and exploring diverse regions of the Pareto set. Experimental results on various two-objective and three-objective benchmark optimization problems with different dimensional complexity characteristics demonstrate that the adaptive particle velocity update strategy improves the search performance of MSCLPSO significantly and is able to help MSCLPSO locate the true Pareto front more quickly and obtain better distributed nondominated solutions over the entire Pareto front.

MULTIOBJECTIVE PARTICLE SWARM OPTIMIZATION BASED ON DIMENSIONAL UPDATE

2013 ◽  
Vol 22 (03) ◽  
pp. 1350015 ◽  
Author(s):  
HEMING XU ◽  
YINGLIN WANG ◽  
XIN XU
Keyword(s):  
Particle Swarm Optimization ◽  
Pareto Front ◽  
Particle Swarm ◽  
Experimental Results ◽  
Entire Region ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Limited Size ◽  
Multiobjective Particle Swarm Optimization ◽  
The Impact

For multiobjective particle swarm optimization (MOPSO), two particles may be incomparable, i. e., not dominated by each other. The personal best and the global best for the particle become less optimal, thus the convergence becomes slow. Even worse, an archive of a limited size can not cover the entire region dominated by the Pareto front, the uncovered region can contain unidentifiable nondominated solutions that are not optimal, and thus the precision the algorithm achieves encounters a plateau. Therefore we propose dimensional update, i. e., evaluating the particle's fitness after updating each variable of its position. Separate consideration of the impact of each variable decreases the occurrence of incomparable relations, thus improves the performance. Experimental results validate the efficiency of our algorithm.

scholarly journals Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 597
Author(s):  
Kun Miao ◽  
Qian Feng ◽  
Wei Kuang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Small Region ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Searches ◽  
Engineering Design Problems ◽  
Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.

scholarly journals Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4620 ◽  
Author(s):  
Junfeng Xin ◽  
Jiabao Zhong ◽  
Shixin Li ◽  
Jinlu Sheng ◽  
Ying Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Population Diversity ◽  
Superior Performance ◽  
Selection Strategy ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Change Environment ◽  
Local Searches ◽  
Improved Algorithm

Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the conventional particle swarm optimization algorithm by introducing the greedy mechanism and the 2-opt operation, based on a combination strategy. First, a greedy black box is established for particle initialization, overcoming the randomness of the conventional method and excluding a great number of infeasible solutions. Then the greedy selection strategy and 2-opt operation are adopted together for local searches, to maintain population diversity and eliminate path crossovers. In addition, Monte-Carlo simulations of eight instances are conducted to compare the improved algorithm with other existing algorithms. The computation results indicate that the improved algorithm has the superior performance, with the shortest route and satisfactory robustness, although a fraction of computing efficiency becomes sacrificed. Moreover, the effectiveness and reliability of the improved method is also verified by its multi-sensor-based application to a USV model in real marine environments.

Sign in / Sign up

Export Citation Format

Share Document