In the previous papers,Quantum-inspired multi-objective evolutionary algorithm(QMEA) was proved to be better than conventional genetic algorithms for multi-objective optimization problem.To improve the quality of the ...In the previous papers,Quantum-inspired multi-objective evolutionary algorithm(QMEA) was proved to be better than conventional genetic algorithms for multi-objective optimization problem.To improve the quality of the non-dominated set as well as the diversity of population in multi-objective problems,in this paper,a Novel Cloud -based quantum -inspired multi-objective evolutionary Algorithm(CQMEA) is proposed.CQMEA is proposed by employing the concept and principles of Cloud theory.The algorithm utilizes the random orientation and stability of the cloud model,uses a self-adaptive mechanism with cloud model of Quantum gates updating strategy to implement global search efficient.By using the self-adaptive mechanism and the better solution which is determined by the membership function uncertainly,Compared with several well-known algorithms such as NSGA-Ⅱ,QMEA.Experimental results show that(CQMEA) is more effective than QMEA and NSGA -Ⅱ.展开更多
An adaptive quantum-inspired evolutionary algorithm based on Hamming distance （HD-QEA） was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was...An adaptive quantum-inspired evolutionary algorithm based on Hamming distance （HD-QEA） was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was taken into consideration, and a suitable rotation angle step （RAS） was assigned to each individual according to the Hamming distance. Performance comparisons were conducted among the HD-QEA, a basic quantum-inspired evolutionary algorithm （QEA） and an individual＇s fitness based adaptive QEA. A solid demonstration was provided that the proposed HD-QEA is better than the other two algorithms in terms of the convergence speed and the global optimization capability when they are employed to optimize the network coding resources in multicast networks.展开更多
This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their streng...This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality.Distributed exploration evolves three independent populations by heterogenous operators.Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches.Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents.Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably.Quantum computation is a newly emerging technique,which has powerful computing power and parallelized ability.Therefore,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace.The QDMA integrates the superiorities of distributed,memetic,and quantum evolution.Computational experiments are carried out to evaluate the superior performance of QDMA.The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test.The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.60903168the Scientific Research Fund of Hunan Provincial Education Department of China under Grant No.10B062Guangdong University of Petrochemical Technology Youth innovative personnel training project(NO 2010YC09)
文摘In the previous papers,Quantum-inspired multi-objective evolutionary algorithm(QMEA) was proved to be better than conventional genetic algorithms for multi-objective optimization problem.To improve the quality of the non-dominated set as well as the diversity of population in multi-objective problems,in this paper,a Novel Cloud -based quantum -inspired multi-objective evolutionary Algorithm(CQMEA) is proposed.CQMEA is proposed by employing the concept and principles of Cloud theory.The algorithm utilizes the random orientation and stability of the cloud model,uses a self-adaptive mechanism with cloud model of Quantum gates updating strategy to implement global search efficient.By using the self-adaptive mechanism and the better solution which is determined by the membership function uncertainly,Compared with several well-known algorithms such as NSGA-Ⅱ,QMEA.Experimental results show that(CQMEA) is more effective than QMEA and NSGA -Ⅱ.
基金supported by the National Natural Science Foundation of China (61473179)the Doctor Foundation of Shandong Province (BS2013DX032)the Youth Scholars Development Program of Shandong University of Technology (2014-09)
文摘An adaptive quantum-inspired evolutionary algorithm based on Hamming distance （HD-QEA） was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was taken into consideration, and a suitable rotation angle step （RAS） was assigned to each individual according to the Hamming distance. Performance comparisons were conducted among the HD-QEA, a basic quantum-inspired evolutionary algorithm （QEA） and an individual＇s fitness based adaptive QEA. A solid demonstration was provided that the proposed HD-QEA is better than the other two algorithms in terms of the convergence speed and the global optimization capability when they are employed to optimize the network coding resources in multicast networks.
基金the National Natural Science Foundation of China(No.62273193)the Talent Introducing Project of Hebei Agricultural University(Nos.KY201903 and YJ201953).
文摘This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality.Distributed exploration evolves three independent populations by heterogenous operators.Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches.Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents.Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably.Quantum computation is a newly emerging technique,which has powerful computing power and parallelized ability.Therefore,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace.The QDMA integrates the superiorities of distributed,memetic,and quantum evolution.Computational experiments are carried out to evaluate the superior performance of QDMA.The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test.The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.
