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1. Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.

Author

Kok, Kai Yit and Rajendran, Parvathy

Subjects
DRONE aircraft, DIFFERENTIAL evolution, PARAMETER estimation, MATHEMATICAL optimization, ROBOTIC path planning
Abstract

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. [ABSTRACT FROM AUTHOR]

Published
2016
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2. Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

Author

Xuan Zhang and Xiang Yu

Subjects
Inertia, Computer science, Evolutionary algorithm, MathematicsofComputing_NUMERICALANALYSIS, lcsh:Medicine, 02 engineering and technology, Infographics, Multi-objective optimization, 0202 electrical engineering, electronic engineering, information engineering, Multi-swarm optimization, lcsh:Science, Problem Solving, Multidisciplinary, Applied Mathematics, Simulation and Modeling, Physics, Pareto principle, Classical Mechanics, Software Engineering, Swarm behaviour, Particle swarm optimization, Charts, Differential evolution, Physical Sciences, Engineering and Technology, 020201 artificial intelligence & image processing, Algorithms, Research Article, Optimization, Computer and Information Sciences, Mathematical optimization, 020209 energy, Research and Analysis Methods, Decision Support Techniques, Motion, Computational Techniques, Humans, Learning, Computer Simulation, Metaheuristic, Genetic Algorithms, Data Visualization, lcsh:R, Models, Theoretical, Probability Theory, Probability Distribution, Source Code, lcsh:Q, Evolutionary Algorithms, Evolutionary Computation, Mathematics
Abstract

Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

Published
2017