Multiple trial vectors in differential evolution for engineering design

Mezura-Montes, Efrén; Coello, Carlos A. Coello; Velázquez-Reyes, Jesús; Muñoz-Dávila, Lucía

doi:10.1080/03052150701364022

Cited by 115 publications

(17 citation statements)

References 19 publications

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“…Hybridization of these algorithms, either with typical computational or bio-inspired approaches, have become more popular. These are DE [8], HS [9], GA [10], PSO [11], ABC [12,13,24], and some hybrid algorithms [14,15,31,32]. These algorithms, also called general swarm intelligence, work based on some successful characteristics of a biological system such as bees, birds, fishes, animals, etc.…”

Section: Bees-inspired Learning Algorithmsmentioning

confidence: 99%

“…Prediction Curves of ABC and QABC algorithms on Al Rajhi prices. Figures 7,8, and 10 present the effect of the parameters quick and gbest-guided on Zain and Al Rajhi future values prediction, respectively. From the above tables values and figures, the performance of the proposed QGGABC algorithm successfully reached minimum training and testing prediction error, fast convergence, high success rate, high accuracy and maximum SNR values on STC, Zain, SAPCO, and Almarai stock prices.…”

Section: Experimental Evaluation and Analysismentioning

confidence: 99%

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A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

et al. 2018

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The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. As it is quite important to know that nowadays, stock market prediction of trends is a significant financial global issue. The scientists, finance administration, companies, and leadership of a given country struggle towards developing a strong financial position. Several technical, industrial, fundamental, scientific, and statistical tools have been proposed and used with varying results. Still, predicting an exact or near-to-exact trend of the Stock Market values behavior is an open problem. In this respect, in the present manuscript, we propose an algorithm based on ABC to minimize the error in the trend and actual values by using the hybrid technique based on neural network and artificial intelligence. The presented approach has been verified and tested to predict the accurate trend of Saudi Stock Market (SSM) values. The proposed QGGABC-ANN based on bio-inspired learning algorithm with its high degree of accuracy could be used as an investment advisor for the investors and traders in the future of SSM. The proposed approach is based mainly on SSM historical data covering a large span of time. From the simulation findings, the proposed QGGABC-FFNN outperformed compared with other typical computational algorithms for prediction of SSM values.

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Section: Bees-inspired Learning Algorithmsmentioning

confidence: 99%

Section: Experimental Evaluation and Analysismentioning

confidence: 99%

A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

et al. 2018

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“…Secondly, εDE-NNC is compared with five other DE algorithms. The five DE algorithms are: (1) multiple trial vectors differential evolution (MDDE) [9], (2) differential evolution with level comparison (DELC) [26], (3) constrained modified differential evolution (COMDE) [27], (4) multi-view differential evolution (MVDE) [28], and (5) improved constrained differential evolution (rank-iMDDE) [29]. In these experiments, the optimization termination is controlled by the maximum number of evaluations, MaxNEs.…”

Section: Test Problems and Experimental Conditionsmentioning

confidence: 99%

“…The advantage of DE is that it has simple structure, requires few control parameters and highly supports parallel computation [6]. Together with the constraint-handling techniques, DE has been applied to the COPs [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Engineering optimization by constrained differential evolution with nearest neighbor comparison

Pham

2016

Vietnam J. Mech.

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It has been proposed to utilize nearest neighbor comparison to reduce the number of function evaluations in unconstrained optimization. The nearest neighbor comparison omits the function evaluation of a point when the comparison can be judged by its nearest point in the search population. In this paper, a constrained differential evolution (DE) algorithm is proposed by combining the ε constrained method to handle constraints with the nearest neighbor comparison method. The algorithm is tested using five benchmark engineering design problems and the results indicate that the proposed DE algorithm is able to find good results in a much smaller number of objective function evaluations than conventional DE and it is competitive to other state-of-the-art DE variants.

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“…In the case in which NLP has general constraints, we refer to Koziel and Michalewicz (), Chandra and Ozdamar (), Hedar and Fukushima (), Mezura‐Montes and Coello Coello (), Mezura‐Montes et al. (), Runarsson and Yao (), and Zhang et al. (), just to mention a few.…”

Section: Introductionmentioning

confidence: 99%

A stochastic local search algorithm for constrained continuous global optimization

Melo

Fampa

Raupp

2012

Int Trans Operational Res

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This paper presents a new stochastic local search algorithm known as feasible-infeasible search procedure (FISP) for constrained continuous global optimization. The proposed procedure uses metaheuristic strategies for combinatorial optimization as well as combined strategies for exploring continuous spaces, which are applied to an efficient process in increasingly refined neighborhoods of current points. We show effectiveness and efficiency of the proposed procedure on a standard set of 13 well-known test problems. Furthermore, we compare the performance of FISP with SNOPT (sparse nonlinear optimizer) and with few successful existing stochastic algorithms on the same set of test problems.

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Multiple trial vectors in differential evolution for engineering design

Cited by 115 publications

References 19 publications

A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

Engineering optimization by constrained differential evolution with nearest neighbor comparison

A stochastic local search algorithm for constrained continuous global optimization

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