Self-adaptive memetic algorithm: an adaptive conjugate gradient approach

Shahidi, Narges; Esmaeilzadeh, Hadi; Abdollahi, Marziye; Ebrahimi, Elham; Lucas, Caro

doi:10.1109/iccis.2004.1460378

Cited by 21 publications

(12 citation statements)

References 6 publications

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“…Salajegheh and Salajegheh et al [64] combined the quasiNewton method and PSO algorithm to improve the performance and reliability of the fundamental PSO. Shahidi [65] proposed a self-adaptive optimization algorithm that uses the conjugate gradient to perform local searches. Ibtissem and Nouredine et al [66] combined the DE algorithm and CGM to improve the fundamental DE's local search capability.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Chaotic Gradient-Based Optimizer for Feature Selection

et al. 2022

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In many applications, selecting the optimal features is a difficult task. Numerous In optimization problems, e.g., feature selection (FS) problem, have been solved using optimization algorithms. In this paper, the most discriminating features were chosen using a new chaotic gradient-based optimizer (CGBO) that combines chaotic maps with searching iterations of the gradient-based optimizer (GBO). Ten chaotic maps were utilised to update the parameters, eliminate a local optimum and premature convergence, accelerate convergence, and enhance the efficiency of GBO. A classifier was used in FS approaches to determine the best subset of characteristics. The proposed CGBO uses the k-nearest neighbor as an objective function for the classification process in FS. Ten datasets from the UCI machine learning repository were used to validate CGBO. In an experiment, CGBO outperformed five other metaheuristic algorithms: particle swarm optimization(PSO), moth flame optimizer(MFO), sine cosine algorithm(SCA), salp swarm algorithm(SSA), and GBO. The results demonstrated the capability of CGBO to find and select the optimal feature subset, which maximized the classification performance and minimized the number of features selected efficiently compared with other metaheuristic algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Chaotic Gradient-Based Optimizer for Feature Selection

et al. 2022

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“…The optimal solution using a gradient-based optimization algorithm is found by determining an extreme point at which the gradient is equal to zero. In the gradient-based optimization method, a search direction is selected and the search process moves along this direction toward the optimal solution ( Shahidi et al, 2005 ). In the metaheuristic algorithm, the initial solution (i.e., the initial population) is randomly generated and the search direction is determined from the results of previous searches.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage

Zheng

Wang

2021

Front. Genet.

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In the era of big data, new storage media are urgently needed because the storage capacity for global data cannot meet the exponential growth of information. Deoxyribonucleic acid (DNA) storage, where primer and address sequences play a crucial role, is one of the most promising storage media because of its high density, large capacity and durability. In this study, we describe an enhanced gradient-based optimizer that includes the Cauchy and Levy mutation strategy (CLGBO) to construct DNA coding sets, which are used as primer and address libraries. Our experimental results show that the lower bounds of DNA storage coding sets obtained using the CLGBO algorithm are increased by 4.3–13.5% compared with previous work. The non-adjacent subsequence constraint was introduced to reduce the error rate in the storage process. This helps to resolve the problem that arises when consecutive repetitive subsequences in the sequence cause errors in DNA storage. We made use of the CLGBO algorithm and the non-adjacent subsequence constraint to construct larger and more highly robust coding sets.

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“…However, the ABC algorithm is good at exploration but poor at exploitation [10]. Integrating gradient-based searching method into evolution algorithms may improve exploitation [11][12][13][14]. In recent years, some novel algorithms based on gradient information have been brought forward to modify the ABC algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

et al. 2020

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The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee colony (GABC) algorithm goes further by modifying the solution search equation. However, the coefficient in its equation is based only on a numerical test and is not suitable for all problems. Therefore, we propose a novel algorithm named the Gbest-guided ABC algorithm with gradient information (GABCG) to make up for its weakness. Without coefficient factors, a new solution search equation based on variable gradients is established. Besides, the gradients are also applied to differentiate the priority of different variables and enhance the judgment of abandoned solutions. Extensive experiments are conducted on a set of benchmark functions with the GABCG algorithm. The results demonstrate that the GABCG algorithm is more effective than the traditional ABC algorithm and the GABC algorithm, especially in the latter stages of the evolution.

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Self-adaptive memetic algorithm: an adaptive conjugate gradient approach

Cited by 21 publications

References 6 publications

An Efficient Chaotic Gradient-Based Optimizer for Feature Selection

An Efficient Chaotic Gradient-Based Optimizer for Feature Selection

CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage

A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

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