Earticle

Differential evolution (DE) is an uncomplicated and serviceable developmental algorithm. Nevertheless, its execution depends on strategies and regulating structures. The combination of several strategies between subpopulations helps to stabilize the probing on DE. In this paper, we propose a unique k-mean soft island model DE(KSDE) algorithm which maintains population diversity through soft island model (SIM). A combination of various approaches, called KSDE, intended for migrating the subpopulation information through SIM is developed in this study. First, the population is divided into k subpopulations using the k-means clustering algorithm. Second, the mutation pattern is singled randomly from a strategy pool. Third, the subpopulation information is migrated using SIM. The performance of KSDE was analyzed using 13 benchmark indices and compared with those of high-technology DE variants. The results demonstrate the efficiency and suitability of the KSDE system, and confirm that KSDE is a cost-effective algorithm compared with four other DE algorithms.

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

근래에 게임이론 분야에서 진화계산법을 사용한 교섭게임 분석은 중요한 이슈 중에 하나이다. 본 논문에서는 이질적인 두 인공 에이전트 간의 공진화를 활용하여 교섭게임을 관찰한다. 두 인공 에이전트를 모델링하기 위해 사용된 전략은 진화전략의 종류인 입자군집최적화와 차분진화알고리즘이다. 교섭게임에서 각 전략이 최선의 결과를 얻기 위한 알고리즘 모수들을 조사하고 두 전략의 공진화를 관찰하여 어느 알고리즘이 교섭게임에 더 우수한지 관찰한다. 컴퓨터 시뮬레이션 실험 결과 입자군집최적화 전략이 차분진화알고리즘 전략보다 교섭게임에서 더 우수한 성능을 보임을 확인하였다.

Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

Tracking articulated hand motion from visual observations is challenging mainly due to the high dimensionality of the state space. Dense sampling is difficult to be performed in such high-dimensional space, so the traditional particle filtering can’t track articulated motion well. In this paper, we propose a new algorithm by combining differential evolution with a particle filter, to track the articulated motion of a hand from single depth images captured by a Kinect sensor. Through the optimization procedure of differential evolution, the particles are moved to the regions with a high likelihood. Only single depth information is used as the input, so our method is immune to illumination and background changes. The tracking system is developed with OpenSceneGraph (OSG). Experiments based on both synthetic and real image sequences demonstrate that the proposed method is capable of tracking articulated hand motion accurately and robustly.

In order to improve the weak situation of the global search ability, the stability and time consuming of optimization of differential evolution(DE) algorithm in solving high dimensional optimization problem, an improved differential evolution algorithm with multi- population and multi-strategy(MPMSIDE) is proposed to solve high dimensional optimization problem. Firstly, the different DE mutation strategies are studied. Then the MPMSIDE algorithm divides the population into several sub-populations, which evolve independently and communicate with each other at regular intervals by using different DE strategies, in order to save the computation time. And the improved mutation strategy and local optimization strategy are introduced to raise and balance the global searching ability and local searching ability, and improve the optimization efficiency. The selfadaptive update strategy is used to adjust the scaling factor and crossover factor for making the parameter sensitivity of DE algorithm and improving the stability and robustness. Finally, the proposed MPMSIDE algorithm is applied to standard test function optimization for verifying the effectiveness. The experimental results show that the proposed MPMSIDE algorithm has a relatively better optimization performance for solving complex optimization problem, and takes on remarkable optimizing ability, higher searching accuracy and faster convergence speed.

In order to improve the optimum speed, crease the diversity of the population and overcome the premature convergence problem in differential evolution(DE) algorithm for solving the complex optimization problems, the chaotic optimization algorithm with powerful local searching capacity and multi- strategy are introduced into the DE algorithm in order to propose an improved adaptive differential evolution(COMSIADE) algorithm in solving function optimization problems. In the COMSIADE algorithm, the ergodicity, regularity and internal randomness of the chaotic sequence are used to overcome the shortcoming of premature local optimum to improve the global searching capacity of the DE algorithm. The multi-population with parallel evolution is used to preserve the diversity of the population at the initial generation. The self-adaptive crossover operator probability is used to improve the global convergence ability, the stability and robustness. Finally, in order to test and verify the effectiveness of the COMSIADE algorithm, several benchmark functions are selected in this paper. The experimental results indicate that the proposed COMSIADE algorithm can improve the global searching capacity and avoid falling into local optimum. And it takes on the higher searching precision and faster convergence speed in solving the complex optimization problem.

Localization as a key problem of wireless sensor network technology, has been the subject of widespread attention. This paper presents an improved positioning algorithm of wireless sensor network based on differential evolution(DE). Firstly, the algorithm improves on each jump distance measurement method in the traditional DV-hop algorithm, thereby reducing distance estimation error between anchor nodes and the unknown nodes, and then through the DE algorithm to obtain better positioning accuracy. Simulation results show that improved positioning algorithm has improved significantly in positioning accuracy compared to the previous DE positioning algorithm and least-squares algorithm, under different error factor and the density of anchor nodes. The algorithm has features of good robustness, global search capability and the ability to inhibit the accumulation of errors, suitable for a variety of applications in wireless sensor networks localization.

An improved wireless sensor networks effective coverage method based on differential evolution algorithm is proposed in this paper, which using quadratic interpolation method to improve the algorithm performance to further optimize the effective coverage of wireless sensor network nodes. Simulation results show that, compared to DE algorithm, the new algorithm can improve local search capabilities, reduce the computational cost of the algorithm, accelerate standards DE convergence, improve the accuracy of solution, and has some reference value for optimization application of wireless sensor network.

The radial basis function neural network (RBFNN) is a great potential artificial intelligence technology and can effectively realize the fault diagnosis for small sample and nonlinear problem. But the parameters of RBFNN model seriously affects the generalization ability and diagnosis accuracy on the great extent. So an improved differential evolution algorithm based on dynamic adaptive adjustment strategy is proposed to optimize the parameters of RBFNN model for obtaining the optimal RBFNN (DASDERBFNN) method. Then the proposed DASDERBFNN method is used to construct a new fault diagnosis (DSDRBFNFD) method. In the DSDRBFNFD method, the dynamic adaptive adjustment strategy is used to adaptively adjust the crossover probability (CR ) value according to the fitness value of current individual in the population for obtaining the improved DE(DASDE) algorithm. Then the selection of parameters in the RBFNN is regarded as a combination optimization of parameters in order to establish the objective function of combination optimization. The DASDE algorithm is used to search for the optimal value of objective function to obtain the better parameter optimization of the RBFNN (DASDERBFNN), which is applied in the fault diagnosis for constructing a new fault diagnosis (DSDRBFNFD) method. Finally, the proposed DSDRBFNFD method is used to diagnose the fault of the cylinder of the engine in order to validate the diagnosis effectiveness of the DSDRBFNFD method. The experiment results show that the proposed DSDRBFNFD method can obtain the higher accuracy of fault diagnosis and is effective fault diagnosis for the engine.

In this paper, a differential evolution (DE) algorithm combined with Lévy flight is proposed to solve the reliability redundancy allocation problems. The Lévy flight is incorporated to enhance the ability of global search of differential evolution algorithm. DE is used for local search mainly. The method considers the trade-off of the diversification and the intensification simultaneously. Experimental results for three benchmark problems demonstrate that the proposed algorithm is more effective for solving the reliability redundancy allocation problems.

The joint replenishment problem with deterministic resource restriction is considered. We present a differential evolution (DE) algorithm that uses indirect grouping strategy to solve constrained joint replenishment. The procedure and structure of the DE algorithm is proposed. Extensive computational experiments are performed to compare the performances of the DE algorithm with results of genetic algorithm (GA) and heuristic algorithm CRAND. The experimental results indicate that the DE algorithm performs relative to CRAND and superior to GA.

This paper proposes a Modified Differential Evolution (MDE) for solving multi-objective load dispatch (MOLD) problem where transmission power losses are considered. MDE is an improved version of conventional Differential Evolution (CDE) in which the mutation operation of the CDE is improved by using five differential solutions instead of three ones similar to CDE. In the MOLD problem, three cases of dispatch including economic dispatch, emission dispatch and multi-objective dispatch are carried out by considering fuel cost function, emission function and both fuel cost and emission functions. In the third case of dispatch, there is a price penalty factor employed to determine the best compromise solution instead of using Fuzzy-based mechanism similar to other studies. The performance of MDE is verified by testing on two systems with three units and one system with six units. In the two systems, the fuel cost and emission from MDE are compared to those from CDE and other existing meta-heuristic algorithms, and the analysis on the result comparison indicates that the MDE is a promising algorithm for solving the MOLD problem.

To solve the slow convergence speed, low precision in later period and tedious parameter setting of differential evolution when applied to complex optimization functions, an improved differential evolution algorithm (dn-DADE) based on dynamic adaptive strategy is proposed. Firstly, the elite solutions of current population are utilized in the new mutation strategy (DE/current-to-dnbest/1) to guide the search direction, and then these optional elite solutions tend to the global optimal solution in the late stage of evolution to balance the diversity of population and convergence speed. Secondly, the adaptive update strategies of scaling factor and crossover factor are designed for control parameter values self-adapting at different search stages, thus improve the stability and robustness of the algorithm. A set of 14 benchmark functions is adopted to test the performance of the proposed algorithm. The results show that dn-DADE algorithm has the advantages of remarkable optimizing ability, higher search precision, faster convergence speed and outperforms several state-of-the-art improved differential evolution algorithms in terms of the main performance indexes.

Heuristic evolutionary optimization algorithms are the solutions to many engineering optimization problems. Differential evolution (DE) is a real stochastic evolutionary parameter optimization in current use.DE does not require more control parameters compared to other evolutionary algorithms. Master Production Scheduling (MPS) is posed as one of multi objective parameter optimization problems and often require an optimal solution for the success of a business organization by balancing demand and supply. This work reviews some of the fundamental theory of differential evolution, the methodology for master production scheduling calculation and most important results. The results available for the existing algorithms are compared with results obtained by the proposed evolutionary algorithm. The analysis reveals that the DE algorithm provides a better solution with reasonable computational time.

Differential evolution (DE) algorithms have been extensively and frequently applied to solve optimizationproblems. Theoretical analyses of their properties are important to understand the underlying mechanismsand to develop more efficient algorithms. In this paper, firstly, we introduce an absorbing Markovsequence to model a DE algorithm. Secondly, we propose and prove two theorems that provide sufficientconditions for DE algorithm to guarantee converging to the global optimality region. Finally, we design two DE algorithms that satisfy the preconditions of the two theorems, respectively. The two proposed algorithmsare tested on the CEC2013 benchmark functions, and compared with other existing algorithms.Numerical simulations illustrate the converge, effectiveness and usefulness of the proposed algorithms.

DE is a topic of current interest in the optimization field. It is the most capable evolutionary algorithm based on biological theory of evolution because of its ease and competence in solving variety of problems, like multi-objective, multi-modal, dynamic optimization problems. But premature convergence or stagnation is a main problem with it. So In order to improve the performance of DE, significant number of DE variants has been proposed by many researchers over the last few decades. Mutation is one of the key tasks of DE. It appreciably influences the performance of DE. In this paper, DE variants with four different mutation techniques- DE/rand/1, DE/local-to-best, DE/either-or and MODE are studied and implemented. Comparison of DE having these mutation strategies is made for variety of dimension and population size and results shows that DE/local-to-best performs best on all the benchmark functions where as MODE also show significant performance.

DE is a topic of current interest in the optimization field. It is the most capable evolutionary algorithm based on biological theory of evolution because of its ease and competence in solving variety of problems, like multi-objective, multi-modal, dynamic optimization problems. But premature convergence or stagnation is a main problem with it. So In order to improve the performance of DE, significant number of DE variants has been proposed by many researchers over the last few decades. Mutation is one of the key tasks of DE. It appreciably influences the performance of DE. In this paper, DE variants with four different mutation techniques- DE/rand/1, DE/local-to-best, DE/either-or and MODE are studied and implemented. Comparison of DE having these mutation strategies is made for variety of dimension and population size and results shows that DE/local-to-best performs best on all the benchmark functions where as MODE also show significant performance.

In order to improve the problem of premature convergence and computational efficiency of traditional differential evolution algorithm in solving high-dimensional problems, an improved differential evolution (HMSDE) algorithm based on combing elite synergy strategy, multi-population strategy and dynamic adaptive strategy is proposed in this paper. In the proposed HMSDE algorithm, the population is dynamically divided into multi-populations in order to keep the diversity of the population, elite synergy strategy is used to achieve information exchange among different sub-populations, and dynamic adaptive strategy is used to dynamically control the parameter values of scaling factor and crossover factor in order to improve the stability and robustness of the HMSDE algorithm. In order to test the performance of the HMSDE algorithm, a set of 10 benchmark functions are selected in here. The results show that the HMSDE algorithm takes on remarkable optimized ability, faster convergence speed and higher search accuracy. And the HMSDE algorithm can avoid the premature convergence and outperforms several state-of-the-art performances.

In order to improve the global searching ability of differential evolution algorithm in solving complex optimization problem, an improved differential evolution (SMDE) algorithm based on the self-adaptive method and multi-population is proposed in this paper. In the proposed SMDE algorithm, the population is divided into multi-populations in order to keep the diversity, then the self-adaptive method is used to control the parameters of differential evolution algorithm in order to balance the local search and global search ability. Finally, several complex benchmark functions are selected to validate the efficiency of the SMDE algorithm. The experiment results show that the proposed SMDE algorithm is better at the global convergence ability and the searching precision.

In this paper we propose a novel differential evolution algorithm based on self-learning, in order to improve the environment adaptive ability of the population in dynamic optimization. The proposed algorithm can monitor the environment changes using re-evaluation of individuals. We direct the population evolution based on the current best individual and another two random individuals, so that the convergence speed is faster and the diversity of the population is maintained. In this way we may reduce the influence from the frequent environment changes. Testing on six dynamic functions, we study the influences caused by period and dimensions. We also compared the proposed algorithm with existing algorithms, the experimental results show that our algorithm has a better environment adaptive ability and achieves better optimization result.