Multiobjective quantum-inspired evolutionary algorithm for fuzzy path planning of mobile robot

Kim, Ye-Hoon; Kim, Jong-Hwan

doi:10.1109/cec.2009.4983080

Cited by 22 publications

(11 citation statements)

References 17 publications

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“…Kim et al [60] aggregated the objective functions using fuzzy measure and fuzzy integral to represent the interactions among objectives. The performance of their approach was demonstrated on a fuzzy path planner for the shooting behavior of a soccer robot [95] by comparison with that of NSGA-II, multi-objective population-based incremental learning (MOPBIL) [96], and multi-objective quantuminspired evolutionary algorithm (MQEA) [97,98]. Meanwhile, some researchers estimated the overall rank of each solution based on its multiple ranks calculated from scalarizing functions [53,58].…”

Section: Use Of Scalarizing Functionsmentioning

confidence: 99%

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

Min

Park

Jang

et al. 2015

Applied Soft Computing

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Section: Use Of Scalarizing Functionsmentioning

confidence: 99%

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

Min

Park

Jang

et al. 2015

Applied Soft Computing

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“…Due to its unique computational performance, the quantum computing has attracted extensive attention of researchers. Some studies have used quantum ACO algorithm in solving robot path planning [14]. Quantum evolutionary algorithm takes advantages of a dynamic balance between diversification and intensification.…”

Section: Related Workmentioning

confidence: 99%

A Heuristic Operator-Based Quantum Ant Colony Optimization Algorithm for Robot Path Planning

You¹,

Liu²,

Zhang³

2017

IJCA

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“…Quantum inspired genetic/evolutionary algorithms have long been in existence and have been applied for the solution of single and bi-objective discrete optimization problems [12], [13]. Unlike GAs or EAs, where a solution is represented using binary or real variables, solutions in quantum inspired models are represented using a string of Q-bits.…”

Section: Introductionmentioning

confidence: 99%

A steady state decomposition based quantum genetic algorithm for many objective optimization

Ray

Asafuddoula

Isaacs

2013

2013 IEEE Congress on Evolutionary Computation

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Many objective optimization refers to the class of optimization problems involving four or more objectives. Optimal solutions of such problems lie on a hyper surface (the Pareto front) and the dimensionality of the hyper-surface is dependent on the number of objectives in conflict. Identifying well converged and well spread set of solutions spanning the hyper-surface is a non-trivial problem. In this paper we introduce a novel decomposition based steady state quantum genetic algorithm, wherein systematic sampling is used to generate the reference directions and a small population of quantum individuals (solutions with variables represented as Q-bits) is evolved using a simple variation operator. A solution represented using Q-bits has the ability to probabilistically represent a number of solutions defined through observation. We exploit the benefits of quantum representation within a steady state evolution scheme and illustrate the behavior of the algorithm using discrete formulations of the unconstrained DTLZ2 test problem involving 2, 3, 5, and 8 objectives. In order to illustrate the behavior for constrained optimization problems, we investigate the behavior of the algorithm using the water resource optimization problem involving 5 objectives. A quantum population of 5 individuals has been used to solve all the above problems. Preliminary results on the effects of the size of quantum population and the fidelity of representation are presented in this paper. Finally, a number of possible directions are suggested to further improve the performance of the algorithm.

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Multiobjective quantum-inspired evolutionary algorithm for fuzzy path planning of mobile robot

Cited by 22 publications

References 17 publications

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

A Heuristic Operator-Based Quantum Ant Colony Optimization Algorithm for Robot Path Planning

A steady state decomposition based quantum genetic algorithm for many objective optimization

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