A novel differential search algorithm and applications for structure design

Liu, Jianjun; Wu, Changzhi; Wu, Guoning; Wang, Xiangyu

doi:10.1016/j.amc.2015.06.036

Cited by 137 publications

(48 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed memetic GDE3 algorithm combines opposition-based learning, local search, and the differential evolution based strategy to get the benefits of each approach. Memetic algorithms [52] and differential evolution algorithms [53], [54] have been used in past studies for other problems. This study is the first attempt to use a memetic generalized differential evolution algorithm for this problem.…”

Section: B Optimization Techniques For Sequence Design Of Dna Problemmentioning

confidence: 99%

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

2020

View full text Add to dashboard Cite

Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of DNA computing, and nanotechnology. The quality and reliability of the DNA sequence can directly affect the accuracy of the processing of information stored in sequences. This problem of designing reliable sequences belongs to the NP-hard class of problems. It has many incompatible design criteria, which cannot be optimized at the same time. Many objective evolutionary algorithms can balance conflicting design criteria by using a diverse population of solutions. This paper proposes an opposition-based Memetic Generalized Differential Evolution (MGDE3) to handle four conflicting design criteria for reliable DNA sequence design. Opposition-based learning and local search strategies are suggested to strengthen the explorative and exploitative properties of the proposed MGDE3. The proposed algorithm is bench-marked with small, medium, and large data sets against 7 highly-cited many-objective and multi-objective algorithms. Experimental results and statistical analysis reveal that MGDE3 significantly outperforms the compared algorithms. The proposed method generates reliable real-life sequences of DNA that are substantially better than the DNA sequences generated by other considered algorithms.

show abstract

Section: B Optimization Techniques For Sequence Design Of Dna Problemmentioning

confidence: 99%

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

2020

View full text Add to dashboard Cite

show abstract

“…Tuning these parameters is computationally expensive. For the methods belonging to the second category, probabilistic techniques are utilized to escape from local minima, such as Genetic Algorithm [7][8][9], Ant Colony Optimization [10,11], Simulated Annealing algorithm [12], Artificial Bee Colony algorithm [13][14][15], Particle Swarm Optimization [16,17], Collective neuro-dynamic optimization [18], Artificial algae algorithm [19] and Differential search algorithm [20,21]. However, these methods tend to obtain solution with low accuracy and are computationally expensive due to lack of guidance by gradient during the searching process [22].…”

Section: Q2mentioning

confidence: 99%

A hybrid approach to constrained global optimization

Liu

Zhang²,

et al. 2016

Applied Soft Computing

Self Cite

View full text Add to dashboard Cite

a b s t r a c tIn this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of these box constrained optimization problems, two hybrid methods are introduced, where two different strategies are used to combine limited memory BFGS (L-BFGS) with Greedy Diffusion Search (GDS). The convergence issue of the two hybrid methods is addressed. To evaluate the effectiveness of the proposed algorithm, 18 box constrained and 4 general constrained problems from the literature are tested. Numerical results obtained show that our proposed hybrid algorithm is more effective in obtaining more accurate solutions than those compared to.

show abstract

“…More technically, deep learning-based [63][64][65][66], machine learning [67][68][69], decision making-based theories, feature selection-based solutions [70][71][72], extremer machine learning solutions [73][74][75][76], as well as hybrid searching algorithms that enhanced conventional multilayer perceptron like harris hawks optimization [77,78], whale optimizer [79,80], bacterial foraging optimization [81], chaos enhanced grey wolf optimization [82], moth-flame optimizer [74,83], many-objective sizing optimization [84][85][86][87][88][89], Driven Robust Optimization [90], ant colony optimization [91], and global numerical optimization [92]. These techniques are successfully employed in different aspects such as building design [93][94][95][96][97][98][99][100], image processing/classification [101][102][103][104][105]…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

View full text Add to dashboard Cite

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

show abstract

A novel differential search algorithm and applications for structure design

Cited by 137 publications

References 53 publications

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

A hybrid approach to constrained global optimization

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Contact Info

Product

Resources

About