Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites

Takahama, Tetsuyuki; Sakai, Setsuko

doi:10.1109/cec.2006.1688283

Cited by 249 publications

(127 citation statements)

References 19 publications

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“…For example, it is worth mentioning that during the special session on constrained optimisation held at CEC 2006 [27], the best approach was based on DE [14], as were three of the top five approaches. Also, there are reported works which focus on keeping low the number of evaluations, with approaches based on DE [11], [12], [13].…”

Section: Methodsmentioning

confidence: 99%

“…An alternative fulfilling such conditions is DE. DE has shown its effectiveness in a number of benchmark functions [9] and real-world problems [10], and it has successfully been applied in constrained optimisation, obtaining good solutions and also reducing the number of function evaluations [11], [12], [13], [14]. DE is a population based evolutionary algorithm which is currently deserving the attention of the research community.…”

Section: Introductionmentioning

confidence: 99%

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An evaluation of Differential Evolution in software test data generation

Becerra

Sagarna

Yao

2009

2009 IEEE Congress on Evolutionary Computation

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An evaluation of Differential Evolution in software test data generation

Becerra

Sagarna

Yao

2009

2009 IEEE Congress on Evolutionary Computation

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“…As the formulation for the above robust adaptive beamforming involves equality constraints, high quality solutions can be obtained by constraint handling methods like -constraint with proper control of the parameter [29,30]. In -constraint handling method the relaxation of the constraints is controlled by using the parameter.…”

Section: Solving For Beamformer's Weightmentioning

confidence: 99%

“…where w θ is the top ϑ-th individual and ϑ = 0.5N P where N P denotes the number of individuals in [29,30]: T c ∈ [0.1T max , 0.8T max ] and cp ∈ [2,10]. The algorithmic description of the DE algorithm can be summarized as STEP 1: Set the generation count G = 0, and randomly initialize a population of N P individuals P G = {w 1,G , .…”

Section: Solving For Beamformer's Weightmentioning

confidence: 99%

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Mallipeddi¹,

Lie²,

Suganthan³

et al. 2011

PIER B

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Abstract-The presence of desired signal in the training data for sample covariance matrix calculation is known to lead to a substantial performance degradation, especially when the desired signal is the dominant signal in the training data. Together with the uncertainty in the look direction, most of the adaptive beamforming solutions are unable to approach the optimal performance. In this paper, we propose an evolutionary algorithm (EA) based robust adaptive beamforming that is able to achieve near optimal performance. The essence of the idea is to shape the array beam response such that it has maximum response in the desired signal's angular range and minimum response in the interferences' angular range. In addition, the approach introduces null-response constraints deduced from the array observation to achieve better interference cancelation performance. As a whole, the proposed optimization is solvable using an improved variant of the differential evolution (DE) algorithm. Numerical simulations are also presented to demonstrate the efficacy of the proposed algorithm.

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“…The ɛ-constrained method is employed in a competition on constrained real-parameter optimization in 2006 (Takahama & Sakai, 2006), where a DE variant and a gradient-based mutation operator were employed. An archive is used to store feasible elites in the population.…”

Section: Separation Of Objective Function and Constraintsmentioning

confidence: 99%

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Kuşakçı

Can

2012

scjournal

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Abstract-( Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COPs.)

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Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites

Cited by 249 publications

References 19 publications

An evaluation of Differential Evolution in software test data generation

An evaluation of Differential Evolution in software test data generation

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

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