Memetic Differential Evolution Research Articles

Stock portfolio optimization based on factor analysis and second-order memetic differential evolution algorithm

Stock portfolio optimization based on factor analysis and second-order memetic differential evolution algorithm

Adaptive memetic differential evolution-back propagation-fuzzy neural network algorithm for robot control

This study established an adaptive memetic differential evolution-back propagation-fuzzy neural network (AMDE-BP-FNN) control method to achieve high-efficiency and precise control of robots with complex dynamic characteristics while reducing control costs. The adaptive differential evolution (ADE) method was applied to search the optimal parameters in the global scope and delimited the pseudo-global search scope. The memetic differential evolution (MDE) method was used to search for optimal parameters in the pseudo-global scope, and the probability factor was set to decide whether to use the back propagation (BP) algorithm for online optimization. Finally, simulations, experiments, and real-world applications were conducted. The results indicated the high efficiency, high precision, and viability of the proposed AMDE-BP-FNN method.

Multi-objective memetic differential evolution optimization algorithm for text clustering problems

Multi-objective memetic differential evolution optimization algorithm for text clustering problems

A differential evolution with adaptive neighborhood mutation and local search for multi-modal optimization

In this paper, an adaptive neighborhood mutation based memetic differential evolution is proposed for multimodal optimization. In the proposed method, an adaptive neighborhood mutation (ANM) strategy is devised to allow the individuals to conduct a diverse search at the early stage of evolution and then gradually switch to an intensive search at the later stage of evolution. Further, the ANM is devised such that encouraging promising individuals for exploitation while unpromising individuals for exploration during evolution to appropriate search the multimodal space. In addition, an adaptive Gaussian based local search strategy, which considers the difference between the offspring and their paired individuals with successful replacements, is developed to appropriately improve promising individuals during evolution. The proposed method has been extensively accessed on a suite of twenty multimodal benchmark functions and the performance of which is compared with sixteen related multimodal algorithms. The results clearly demonstrate the superiority of proposed method as well as the merit of devised strategies.

Adaptive sliding mode control with hysteresis compensation-based neuroevolution for motion tracking of piezoelectric actuator

In this paper, an adaptive sliding mode control with hysteresis compensation-based neuroevolution (ACNE) is proposed for precise motion tracking of the piezoelectric actuator (PEA) in the presence of uncertainties, disturbances, and nonlinearity hysteresis characteristics. Firstly, a new memetic differential evolution (MeDE) algorithm is proposed to optimize the weights of a 3-layer neural network (called neuroevolution or NE). In MeDE, a differential evolution algorithm is used as a global search scheme and the Jaya algorithm is used as local search exploitation. Secondly, an inverse hysteresis model of PEA is identified by the neuroevolution model to provide a feed-forward NE control signal to compensate for the hysteresis behavior of PEA system. Thirdly, an adaptive neural sliding mode control plus feedforward NE (ACNE) control is designed to enhance the quality control and guarantee asymptotical stability for PEA system. Based on the Lyapunov method, the stability of the closed-loop system is analyzed and proved. Finally, the experimental Thorlabs piezoelectric actuator (PEA) is set up to verify the robustness and effectiveness of the proposed approach. Results show that the identified MeDE-Neuroevolution model has successfully applied to model the inverse hysteretic of PEA system and the performance of MeDE has better than Jaya, DE, and PSO in terms of best, worst, average, and standard deviation. Furthermore, in motion tracking control, the performance of proposed ACNE control has more accurate than a classical PID control, a feedforward control, a hybrid feedback–feedforward control, and an adaptive neural sliding mode control without a compensator.

Adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization

This paper proposes an adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization. In the proposed algorithm, a multi-niche sampling strategy is designed to sample a subpopulation for evolution at each generation. In this strategy, the entire population is firstly divided into multiple niches by employing a certain niching strategy at each generation. A subpopulation is then dynamically sampled from the resulting niches such that supporting a diverse search at the early stage of evolution while an intensive search towards the end of evolution. The above strategy will be further coupled with a neighborhood crossover, which is devised to encourage high potential solutions for exploitation while low potential solutions for exploration, thus appropriately searching the solution space. Additionally, an adaptive local search (ALS) scheme along with an adaptive elimination operation (AEO) have been designed. The ALS aims to appropriately fine-tune promising solutions in the sampled subpopulation while the AEO tends to adaptively eliminate unpromising individuals in the population during evolution. The performance of the proposed algorithm has been evaluated on CEC’2015 benchmark functions and compared with related methods. Experimental results show that our algorithm can achieve a superior performance and outperform related methods. The results also confirm the significance of devised strategies in the proposed algorithm.

Adaptive memetic differential evolution with niching competition and supporting archive strategies for multimodal optimization

Multimodal optimization, which aims at locating multiple optimal solutions within the search space, is inherently a difficult problem. This work proposes an adaptive memetic differential evolution algorithm with niching competition and supporting archive strategies to tackle the problem. In the proposed algorithm, a niching competition strategy is designed to competitively employ niches according to their potentials by encouraging high potential niches for exploitation while low potential niches for exploration, thus appropriately searching the space to identify multiple optima. Further, a supporting archive strategy is devised and implemented at the niche level with a dual purpose of helping maintain potential optima as well as facilitate the evolution of population. In this strategy, the writing and reading of archive is implicitly implemented during evolution rather than requiring external rules. Additionally, an adaptive Cauchy-based local search scheme, which considers the possible locations of optima to implement the local search, is developed and incorporated into the proposed method to efficiently and properly improve niching seeds. The resulting algorithm has been evaluated with extensive experiments on benchmark functions as well as a robot kinematics problem and compared with related methods. The results show that our method is able to consistently and accurately locate multiple optima in the solution space, and outperform related methods.

A memetic discrete differential evolution algorithm for the distributed permutation flow shop scheduling problem

The distributed manufacturing has become a prevail production mode under the economic globalization. In this article, a memetic discrete differential evolution (MDDE) algorithm is proposed to address the distributed permutation flow shop scheduling problem (DPFSP) with the minimization of the makespan. An enhanced NEH (Nawaz–Enscore–Ham) method is presented to produce potential candidate solutions and Taillard’s acceleration method is adopted to ameliorate the operational efficiency of the MDDE. A new discrete mutation strategy is introduced to promote the search efficiency of the MDDE. Four neighborhood structures, which are based on job sequence and factory assignment adjustment mechanisms, are introduced to prevent the candidates from falling the local optimum during the search process. A neighborhood search mechanism is selected adaptively through a knowledge-based strategy which focuses on the adaptive evaluation for the neighborhood selection. The optimal combinations of parameters in the MDDE algorithm are testified by the design of experiment. The computational results and comparisons demonstrated the effectiveness of the MDDE algorithm for solving the DPFSP.

Memetic differential evolution methods for clustering problems

The Euclidean Minimum Sum-of-Squares Clustering(MSSC) is one of the most important models for the clustering problem. Due to its NP-hardness, the problem continues to receive much attention in the scientific literature and several heuristic procedures have been proposed. Recent research has been devoted to the improvement of the classical K-MEANS algorithm, either by suitably selecting its starting configuration or by using it as a local search method within a global optimization algorithm. This paper follows this last approach by proposing a new implementation of a Memetic Differential Evolution (MDE) algorithm specifically designed for the MSSC problem and based on the repeated execution of K-MEANS from selected configurations. In this paper we describe how to adapt MDE to the clustering problem and we show, through a vast set of numerical experiments, that the proposed method has very good quality, measured in terms of the minimization of the objective function, as well as a very good efficiency, measured in the number of calls to the local optimization routine, with respect to state of the art methods.

Efficient large scale global optimization through clustering-based population methods

Back in the 80’s clustering methods were considered state of the art for non-structured box constrained global optimization (GO). Their disappearance is mainly due to their increasing difficulties in solving even moderately sized GO problems, yet the basic idea was indeed a brilliant one. More recently population methods and Differential Evolution (DE) in particular has gained much attention in the GO heuristic world due to their easy implementation and good exploration capabilities. In order to improve the exploitation capability of DE, some memetic variants have been proposed with success.In this paper we revisit clustering methods and apply them both to standard low dimensional problems and to large scale ones; in particular we propose a novel approach to apply a clustering-type decision on when to start a local search to variants of memetic DE. The resulting algorithm, C-MDE (Clustering Memetic DE) outperforms the best-known methods both in quality of the returned solution and in the number of calls to the expensive local optimization phase, even in large dimension. For large dimensional problems, random projections are used in order to be able to decide on starting a local search on a limited number of features. The resulting GO method is a revisit of clustering techniques in which all of the defects which made those methods no more feasible are eliminated: in particular, the method is used within an adaptive population method and it efficiently runs also in large dimension. Thus we think the proposed approach will find a place in the top-performing modern GO algorithms.

Solving text clustering problem using a memetic differential evolution algorithm.

The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems.

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.

A multilevel sampling strategy based memetic differential evolution for multimodal optimization

Multimodal optimization, aiming to locate multiple optima in parallel, is a challenging task. In this paper, a multilevel sampling strategy based memetic differential evolution algorithm is proposed to tackle the problem. In the proposed algorithm, a multilevel sampling strategy is devised to sample a subpopulation for evolution at each generation. In this strategy, the entire population is dynamically divided into multiple levels according to the fitness of individuals at each generation. A subpopulation is then adaptively sampled from the individuals at different levels to undergo a niching based evolution for identifying multiple optima in the search space. Further, a crossover-based local search scheme is designed to fine-tune the seed solutions of niches in the population during evolution. We evaluate the proposed method on 20 benchmark multimodal problems and compare it with state-of-the-art multimodal optimization algorithms. The results show that our proposed algorithm can effectively and accurately locate multiple optima, outperforming related methods to be compared.

An on-line variable-fidelity surrogate-assisted harmony search algorithm with multi-level screening strategy for expensive engineering design optimization

This paper presents an on-line variable-fidelity surrogate-assisted harmony search algorithm (VFS-HS) for expensive engineering design optimization problems. VFS-HS employs a novel model-management strategy that uses a multi-level screening mechanism based on non-dominated sorting to strictly control the numbers of low-fidelity and high-fidelity evaluations and to keep a balance between exploration and exploitation. The performance of VFS-HS is validated through comparison not only to those of four single-fidelity surrogate-assisted optimization methods (i.e. the particle swarm optimization algorithm with radial basis function-based surrogate (OPUS-RBF), the two-layer surrogate-assisted particle swarm optimization algorithm (TLSAPSO), the surrogate-assisted hierarchical particle swarm optimization (SHPSO) and the hybrid surrogate-based optimization using space reduction (HSOSR)) but also to that a multi-fidelity surrogate-assisted optimization method (the multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution (MGPMDE)) on the CEC2014 expensive optimization test suite. A real-world problem of the optimal design for a long cylindrical gas-pressure vessel is also investigated. The results show that VFS-HS outperforms all the compared methods.

Color image encryption approach based on memetic differential evolution

In this paper, the initial conditions of an intertwining logistic map are generated by utilizing the memetic differential evolution. Initially, the color image is decomposed into red, green, and blue channels. Arnold map is used to shuffle the pixels position of all three channels to develop the shuffled channels. Afterward, memetic differential evolution is implemented to optimize the parameters required by intertwining logistic map. The correlation coefficient and entropy are used as a fitness function. The intertwining map generates the secret keys to encrypt the shuffled color channels. The encrypted color channels are combined to obtain the encrypted image. Extensive experiments are carried out by considering the memetic differential evolution-based image encryption and existing competitive image encryption approaches over five benchmark images. Experimental results show that the proposed approach provides higher efficiency and security as compared to the existing image encryption approaches.

A memetic differential evolution algorithm for energy-efficient parallel machine scheduling

This paper considers an energy-efficient bi-objective unrelated parallel machine scheduling problem to minimize both makespan and total energy consumption. The parallel machines are speed-scaling. To solve the problem, we propose a memetic differential evolution (MDE) algorithm. Since the problem involves assigning jobs to machines and selecting an appropriate processing speed level for each job, we characterize each individual by two vectors: a job-machine assignment vector and a speed vector. To accelerate the convergence of the algorithm, only the speed vector of each individual evolves and a list scheduling heuristic is applied to derive its job-machine assignment vector based on its speed vector. To further enhance the algorithm, we propose efficient speed adjusting and job-machine swap heuristics and integrate them into the algorithm as a local search approach by an adaptive meta-Lamarckian learning strategy. Computational results reveal that the incorporation of list scheduling heuristic and local search greatly strengthens the algorithm. Computational experiments also show that the proposed MDE algorithm outperforms SPEA-II and NSGA-II significantly.

Memetic Differential Evolution with Baldwin Effect and Opposition-Based Learning

Memetic Differential Evolution with Baldwin Effect and Opposition-Based Learning

Memetic Differential Evolution with an Improved Contraction Criterion.

Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts. The proposed algorithm is compared with six well-known evolutionary algorithms on twenty-one benchmark functions, and the experimental results are analyzed with two kinds of nonparametric statistical tests. Moreover, sensitivity analyses for parameters in MDE are also made. Experimental results have demonstrated the competitive performance of the proposed method with respect to the six compared algorithms.

An improved memetic differential evolution for college students' comprehensive quality evaluation

The evaluation of the comprehensive quality of college students is a key problem in the management of college student affairs. In this paper, we present an improved memetic differential evolution algorithm to get the best weights of the College Students' Comprehensive Quality Evaluation (CSCQE) problem. The proposed algorithm, called Uniform Memetic Differential Evolution (UMDE), hybridises differential evolution (DE) with a local search (LS) operator and a periodic uniform design re-initialisation scheme to balance the exploration and exploitation. UMDE is compared with five well-known evolutionary algorithms on twenty-one benchmark functions. The results show that UMDE can obtain results better than, or at least comparable with, the compared algorithms. And then, UMDE is used to solve the CSCQE problem. The results show that UMDE can find better weights of the index system.

An improved memetic differential evolution for college students' comprehensive quality evaluation

The evaluation of the comprehensive quality of college students is a key problem in the management of college student affairs. In this paper, we present an improved memetic differential evolution algorithm to get the best weights of the College Students' Comprehensive Quality Evaluation (CSCQE) problem. The proposed algorithm, called Uniform Memetic Differential Evolution (UMDE), hybridises differential evolution (DE) with a local search (LS) operator and a periodic uniform design re-initialisation scheme to balance the exploration and exploitation. UMDE is compared with five well-known evolutionary algorithms on twenty-one benchmark functions. The results show that UMDE can obtain results better than, or at least comparable with, the compared algorithms. And then, UMDE is used to solve the CSCQE problem. The results show that UMDE can find better weights of the index system.