Gaussian Bare-Bones Differential Evolution

Wang, Hui; Rahnamayan, Shahryar; Sun, Haoyu; Omran, Mahamed G. H.

doi:10.1109/tsmcb.2012.2213808

Cited by 235 publications

(19 citation statements)

References 50 publications

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“…Well-known classical benchmark problems used in the following experiments for studying and comparing the performance of proposed optimization method with other methods in this section [23,28]. Specifically, we have used six well-defined objective functions to test the proposed Firefly method.…”

Section: Other Optimization Methodsmentioning

confidence: 99%

The Enhanced Firefly Algorithm Based on Modified Exploitation and Exploration Mechanism

2018

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As a nature-inspired search algorithm, the Firefly algorithm (being a naturally outstanding search algorithm with few control parameters) may have a considerable influential performance. In this paper, we present a new firefly algorithm to address the parameter selection and adaptation strategy in the standard firefly algorithm. The proposed firefly algorithm introduces a modified exploration and exploitation mechanism, with adaptive randomness and absorption coefficients. The proposed method employs the adaptation of the randomness and absorption coefficients to be a function of time/iterations. Moreover, gray relational analysis advancing fireflies is used to allocate different information from appealing ones effectively. Standard benchmark functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of the proposed firefly algorithm is superior to (or at least highly competitive with) the standard firefly algorithm, and state-of-the-art approaches in terms of performance.

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Section: Other Optimization Methodsmentioning

confidence: 99%

The Enhanced Firefly Algorithm Based on Modified Exploitation and Exploration Mechanism

2018

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“…Based on the idea that the Gaussian sampling is a fine tuning procedure which starts during exploration and is continued to exploitation, Wang et al [22] proposed a new parameter-free DE algorithm, called GBDE. In the GBDE, the mutation strategy uses a Gaussian sampling method which is defined by

\begin{matrix} v_{i, j} (t + 1) \\ = {\begin{matrix} N (\frac{X_{best, j} (t) + x_{i, j} (t)}{normal2}, & rand (0,1) \leq CR \lor j = j_{rand} \\ | X_{best, j} (t) - x_{i, j} (t) |) \\ x_{i, j} (t) & otherwise, \end{matrix} \end{matrix}

where N represents a Gaussian distribution with mean ( X best, j ( t ) + x i , j ( t ))/2 and standard deviation | X best, j ( t ) − x i , j ( t )| and CR is the probability of crossover.…”

Section: Bare-bones Algorithmmentioning

confidence: 99%

“…To balance the global search ability and convergence rate, Wang et al [22] proposed a modified GBDE (called MGBDE). The mutation strategy uses a hybridization of GBDE and DE/best/1 as follows:

\begin{matrix} v_{i, j} (t + 1) \\ = {\begin{matrix} X_{best, j} (t) + F \cdot (x_{i_{normal1}, j} (t) - x_{i_{normal2}, j} (t)) & rand (0,1) \leq normal0.5 \\ N (\frac{X_{best, j} (t) + x_{i, j} (t)}{normal2}, | X_{best, j} (t) - x_{i, j} (t) |) & otherwise . \end{matrix} \end{matrix}

…”

Section: Bare-bones Algorithmmentioning

confidence: 99%

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Bare-Bones Teaching-Learning-Based Optimization

Zou

Wang

Hei

et al. 2014

The Scientific World Journal

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Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

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“…This problem is a highly complex multimodal one having strong epistasis with minimum value f ( X *) = 0 [24]. This problem was frequently solved by EAs or taken as a benchmark real-world optimization problem to test the performance of new EA variants [25, 26].…”

Section: Experimental Study On a Real-world Optimization Problemmentioning

confidence: 99%

Complexity Reduction in the Use of Evolutionary Algorithms to Function Optimization: A Variable Reduction Strategy

Pedrycz

et al. 2013

The Scientific World Journal

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Discovering and utilizing problem domain knowledge is a promising direction towards improving the efficiency of evolutionary algorithms (EAs) when solving optimization problems. We propose a knowledge-based variable reduction strategy (VRS) that can be integrated into EAs to solve unconstrained and first-order derivative optimization functions more efficiently. VRS originates from the knowledge that, in an unconstrained and first-order derivative optimization function, the optimal solution locates in a local extreme point at which the partial derivative over each variable equals zero. Through this collective of partial derivative equations, some quantitative relations among different variables can be obtained. These variable relations have to be satisfied in the optimal solution. With the use of such relations, VRS could reduce the number of variables and shrink the solution space when using EAs to deal with the optimization function, thus improving the optimizing speed and quality. When we apply VRS to optimization problems, we just need to modify the calculation approach of the objective function. Therefore, practically, it can be integrated with any EA. In this study, VRS is combined with particle swarm optimization variants and tested on several benchmark optimization functions and a real-world optimization problem. Computational results and comparative study demonstrate the effectiveness of VRS.

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Gaussian Bare-Bones Differential Evolution

Cited by 235 publications

References 50 publications

The Enhanced Firefly Algorithm Based on Modified Exploitation and Exploration Mechanism

The Enhanced Firefly Algorithm Based on Modified Exploitation and Exploration Mechanism

Bare-Bones Teaching-Learning-Based Optimization

Complexity Reduction in the Use of Evolutionary Algorithms to Function Optimization: A Variable Reduction Strategy

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