Performance evaluation of dynamic multi-swarm particle swarm optimizer with different constraint handling methods on path planning problems

Liang, Jing; Song, Hui; Qu, Boyang

doi:10.1109/mc.2013.6608209

Cited by 4 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So the value of n is very important to the solution to this problem. Because the number n of the portions determines the dimensions of the mission environment, in order to control it and improve the optimization efficiency, n can be limited in [5,15].…”

Section: Environmental Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Modified central force optimization (MCFO) algorithm for 3D UAV path planning

et al. 2016

View full text Add to dashboard Cite

Section: Environmental Modelingmentioning

confidence: 99%

“…In order to simplify the problem, the penalty function method is applied to simplify the constraints [15]. In this way, the constrained optimization problem turns into the unconstrained optimization problem.…”

Section: Optimization Model Formulationmentioning

confidence: 99%

Modified central force optimization (MCFO) algorithm for 3D UAV path planning

et al. 2016

View full text Add to dashboard Cite

“…3 Constraints handling In this paper, the superiority of feasible solutions technique is modified to be adopted to handle constraints [6] in this paper. The dynamic threshold ε changes in the following way: 0 (1 5 / 3 ), 0.6 0, 0.6…”

Section: Classical Representationmentioning

confidence: 99%

Modified Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Mineral Grinding Process

Chen

Zheng

2014

AMR

View full text Add to dashboard Cite

The mineral grinding process is a typical constrained multi-objective optimization problem for its two main goals are quality and quantity. This paper established a similarity criterion mathematical model and combined Multi-objective Dynamic Multi-Swarm Particle Swarm Optimization with modified feasibility rule to optimize the two goals. The simulation results showed that the results of high quality were achieved and the Pareto frontier was evenly distributed and the proposed approach is efficient to solve the multi-objective problem for the mineral grinding process.

show abstract

“…In the previous work, two different constraint handling methods, dynamic threshold and dynamic balance function, have been tested [28]. Based on the analysis on the weakness of these two constraint handling methods, a novel constraint handling method "dynamic compared Δ" is proposed to be incorporated intothe algorithm to improve the search efficiency.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Different Curves Used in Path Planning Problems Based on Particle Swarm Optimizer

Song

Liu

2014

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

In path planning problems, the most important task is to find a suitable collision-free path which satisfies some certain criteria (the shortest path length, security, feasibility, smoothness, and so on), so defining a suitable curve to describe path is essential. Three different commonly used curves are compared and discussed based on their performance on solving a set of path planning problems. Dynamic multiswarm particle swarm optimizer is employed to optimize the necessary parameters for these curves. The results show that Bezier curve is the most suitable curve for producing path for the certain path planning problems discussed in this paper. Safety criterion is considered as a constrained condition. A new constraint handling method is proposed and compared with other two constraint handling methods. The results show that the new method has a better characteristic to improve the performance of algorithm.

show abstract

Performance evaluation of dynamic multi-swarm particle swarm optimizer with different constraint handling methods on path planning problems

Cited by 4 publications

References 19 publications

Modified central force optimization (MCFO) algorithm for 3D UAV path planning

Modified central force optimization (MCFO) algorithm for 3D UAV path planning

Modified Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Mineral Grinding Process

Comparison of Three Different Curves Used in Path Planning Problems Based on Particle Swarm Optimizer

Contact Info

Product

Resources

About