Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure

Wang, Yaonan; Wu, Lianghong; Yuan, Xiaofang

doi:10.1007/s00500-008-0394-9

Cited by 241 publications

(124 citation statements)

References 29 publications

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“…The crowding entropy of elite l can be defined as CE l , the calculation procedure of which is detailed in [24]. The elites with objective function extrema are retained.…”

Section: Elitism-preserving Strategy Based On Crowding Entropymentioning

confidence: 99%

A probabilistic scenario-based framework for solving stochastic dynamic economic emission dispatch with unit commitment

Zhang¹,

Liu²,

Liao³

et al. 2017

Turk J Elec Eng & Comp Sci

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This paper establishes a probabilistic scenario-based framework for the stochastic dynamic economic emission dispatch with unit commitment (SDEED-UC) problem, by considering wind power integration. The scenario generation and reduction method are implemented to describe wind power uncertainty. Accordingly, each wind power scenario is analyzed separately to determine the on/off status of the units. As for a predetermined significance level, the UC scheduling solution can be obtained with a probabilistic point of view, considering all the original scenarios. Then the SDEED problem is converted into a number of deterministic scheduling problems. For each scenario in the reduced set, an enhanced multiobjective particle swarm optimization algorithm is proposed to produce the Pareto optimal solutions.The practicability and performance of the proposed approach are illustrated through a case study, and the results are compared with the existing multiobjective evolutionary algorithms.

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“…The crowding entropy of elite l can be defined as CE l , the calculation procedure of which is detailed in [24]. The elites with objective function extrema are retained.…”

Section: Elitism-preserving Strategy Based On Crowding Entropymentioning

confidence: 99%

A probabilistic scenario-based framework for solving stochastic dynamic economic emission dispatch with unit commitment

Zhang¹,

Liu²,

Liao³

et al. 2017

Turk J Elec Eng & Comp Sci

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show abstract

“…According to the dominance relation between , and , , there may be at most three situations [43]: (1) , dominates , ; (2) , dominates , ; (3) , and , are nondominated with each other; select the vector with the less crowding distance. is chosen from three population vectors randomly to achieve the exploratory searching.…”

Section: Initial Population Vectormentioning

confidence: 99%

“…In order to have a good diversity among generated nondominated solutions in the advanced population of fixed size, the crowding entropy [43] is utilized to evaluate the crowding degree around each nondominated solution. The procedure of the proposed algorithm is described as follows.…”

Section: Selectionmentioning

confidence: 99%

A Multiobjective Evolutionary Algorithm for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Sensor Network

Liu

Qin

et al. 2015

International Journal of Distributed Sensor Networks

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Cognitive radio has emerged as a promising solution to address the problems posed by coming spectrum scarcity for the inherently resource-constrained sensor networks. Reliability and energy consumption are key objectives for spectrum sensing in cognitive sensor networks. In this paper, a fast differential evolution algorithm is proposed to optimize the energy consumption and spectrum sensing performance jointly. By constructing a comprehensive performance metric, the joint optimization is transferred to a multiobjective optimal problem, in which the sleeping schedule and censoring mechanism are taken into consideration. The main objective of the proposed algorithm is to minimize the network energy consumption subjected to constraints on the detection performance by optimally deriving the censoring and sleeping probabilities. To accelerate the convergence speed and maintain the diversity, the algorithm utilizes the advantages of opposite-based learning for generating the initial population and a tournament scheme in mutation step. In the crossover step, a control parameters dynamic adjustment scheme is applied to make a trade-off between exploration and exploitation. Finally, a selection mechanism is introduced for generating a well-distributed Pareto optimal front. The simulation results show that the proposed algorithm can reduce the average energy consumption of cognitive sensor node, while improving the global probability of spectrum sensing.

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“…In solving the multiobjective optimization problems, Farhang-Mehr and Azarm [11] and Gunawan et al [12] have applied the entropy to maintain the diversity of the solution set well in multiobjective problems and multilevel multiobjective problems. Wang et al proposed the MOSADE algorithm [13], which combines the self-adaptive differential evolution and the crowding entropy-based diversity measure to obtain the nondominated solution set. In this algorithm, every solution can calculate its crowding degree through the improved the information entropy formula according to solutions' distribution.…”

Section: Introductionmentioning

confidence: 99%

An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism

Qin

Liu

2012

Applied Computational Intelligence and Soft Computing

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Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multiobjective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the population to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to maintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely used test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and diversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE.

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Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure

Cited by 241 publications

References 29 publications

A probabilistic scenario-based framework for solving stochastic dynamic economic emission dispatch with unit commitment

A probabilistic scenario-based framework for solving stochastic dynamic economic emission dispatch with unit commitment

A Multiobjective Evolutionary Algorithm for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Sensor Network

An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism

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