To solve the synchronisation problem associated with fractional-order hyperchaotic systems, in this study, a new dual-neural network finite-time sliding mode control method was developed, and a differentialevolution ...
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To solve the synchronisation problem associated with fractional-order hyperchaotic systems, in this study, a new dual-neural network finite-time sliding mode control method was developed, and a differential evolution algorithm was used to optimise the switching gain, control parameters, and sliding mode surface parameters, greatly reducing chattering problems in sliding mode controllers. By using the developed method, the complete synchronisation of the drive system and the response system of a fractional-order hyperchaotic system was realised in a finite time;moreover, the stability of the error system under this method was proved by using Lyapunov stability theorem. Numerical simulation results verified the feasibility and superiority of the method.
An improved differential evolution algorithm (IDE) is proposed to solve task assignment problem. The IDE is an improved version of differential evolution algorithm (DE), and it modifies two important parameters of DE ...
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An improved differential evolution algorithm (IDE) is proposed to solve task assignment problem. The IDE is an improved version of differential evolution algorithm (DE), and it modifies two important parameters of DE algorithm: scale factor and crossover rate. Specially, scale factor is adaptively adjusted According to the objective function values of all candidate solutions, and crossover rate is dynamically adjusted with the increasement of iterations. The adaptive scale factor and dynamical crossover rate are combined to increase the diversity of candidate solutions, and to enhance the exploration capacity of solution space of the proposed algorithm. In addition, a usual penalty function method is adopted to trade-off the objective and the constraints. Experimental results demonstrate that the optimal solutions obtained by the IDE algorithm are all better than those obtained by the other two DE algorithms on solving some task assignment problems. (C) 2011 Elsevier Ltd. All rights reserved.
Cellular manufacturing (CM) is an important application of group technology (GT), a manufacturing philosophy in which parts are grouped into part families, and machines are allocated into machine cells to take advanta...
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Cellular manufacturing (CM) is an important application of group technology (GT), a manufacturing philosophy in which parts are grouped into part families, and machines are allocated into machine cells to take advantage of the similarities among parts in manufacturing. The target is to minimize inter-cellular movements. Inspired by the rational behind the so called grouping genetic algorithm (GGA), this paper proposes a grouping version of differentialevolution (GDE) algorithm and its hybridized version with a local search algorithm (HGDE) to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 40 problem instances from literature and compare the performance of GGA and GDE. We also compare the performance of both algorithms when they are tailored with a local search algorithm. Our computations reveal that the proposed algorithm performs well on all test problems, exceeding or matching the best solution quality of the results presented in previous literature. (C) 2009 Elsevier Ltd. All rights reserved.
This article proposes a differential evolution algorithm (DE) for solving type 1 simple assembly line balancing problem (SALBP-1). The proposed heuristic composes of four main steps: (1) initialization, (2) mutation, ...
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This article proposes a differential evolution algorithm (DE) for solving type 1 simple assembly line balancing problem (SALBP-1). The proposed heuristic composes of four main steps: (1) initialization, (2) mutation, (3) recombination, and (4) selection process. A new decoding scheme is proposed along with new recombination formulas besides those found in literatures. The computational results based on many tests using set of standard instances show that the proposed DE algorithm is very competitive for solving SALPB-1.
This paper proposes the application of differentialevolution (DE) algorithm for the optimal tuning of proportional-integral (PI) controller designed to improve the small signal dynamic response of a stand-alone solid...
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This paper proposes the application of differentialevolution (DE) algorithm for the optimal tuning of proportional-integral (PI) controller designed to improve the small signal dynamic response of a stand-alone solid oxide fuel cell (SOFC) system. The small signal model of the study system is derived and considered for the controller design as the target here is to track small variations in SOFC load current. Two PI controllers are incorporated in the feedback loops of hydrogen and oxygen partial pressures with an aim to improve the small signal dynamic responses. The controller design problem is formulated as the minimization of an eigenvalue based objective function where the target is to find out the optimal gains of the PI controllers in such a way that the discrepancy of the obtained and desired eigenvalues are minimized. Eigenvalue and time domain simulations are presented for both open-loop and closed loop systems. To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm. Three different types of load disturbances are considered for the time domain based results to investigate the performances of different optimizers under different sorts of load variations. Moreover, non-parametric statistical analyses, namely, one sample Kolmogorov-Smirnov (KS) test and paired sample t test are used to identify the statistical advantage of one optimizer over the other for the problem under study. The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution.
Fuzzy classification rule mining can be considered as a challenging optimization problem with the purpose of extracting accurate and interpretable rules. This paper deals with a Michigan cooperative approach for minin...
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Fuzzy classification rule mining can be considered as a challenging optimization problem with the purpose of extracting accurate and interpretable rules. This paper deals with a Michigan cooperative approach for mining fuzzy rules. The proposed algorithm named EDE-FRMiner uses an enhanced differentialevolution that evolves population of individuals;each one represents a single rule. The whole population collaborates to generate in one shot an accurate and reduced number of rules. EDE-FRMiner is an intelligent process of evolution that uses fast arithmetic operators and a new cooperative weights memory. This latter allows sharing information between individuals. In addition, it uses a new threshold based fitness function using a redefined support and confidence measures. The adaptive threshold mechanism used in the fitness function aims to adapt the miner system to problems with dynamic training data. Experiments are carried out using the NSL-kdd'99 intrusion detection data set and other data sets from the UCI repository. A comparative study with other competitive evolutionary rule based systems is performed and the results show the effectiveness of proposed algorithm.
Genetic algorithms (GAS), particle swarm optimisation (PSO) and differentialevolution (DE) have proven to be successful in engineering optimisation problems. The limitation of using these tools is their expensive com...
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Genetic algorithms (GAS), particle swarm optimisation (PSO) and differentialevolution (DE) have proven to be successful in engineering optimisation problems. The limitation of using these tools is their expensive computational requirement. The optimisation process usually needs to run the numerical model and evaluate the objective function thousands of times before converging to an acceptable solution. However, in real world applications, there is simply not enough time and resources to perform such a huge number of model runs. In this study, a computational framework, known as DE-kNN, is presented for solving computationally expensive optimisation problems. The concept of DE-kNN will be demonstrated via one novel approximate model using k-Nearest Neighbour (kNN) predictor. We describe the performance of DE and DE-kNN when applied to the optimisation of a test function. The simulation results suggest that the proposed optimisation framework is able to achieve good solutions as well as provide considerable savings of the function calls compared to DE algorithm. (C) 2010 Elsevier Ltd. All rights reserved.
To implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization (PSODE) is proposed. In the PSODE, control parameters are encoded to be a symbioti...
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To implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization (PSODE) is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. differentialevolution ( DE) operators are used to evolve the original population. And, particle swarm optimization (PSO) is applied to co-evolving the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functious. The results show that the average performance of PSODE is the best.
An improved differentialevolution(IDE)algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem(RCPSP)with the obj...
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An improved differentialevolution(IDE)algorithm that adopts a novel mutation strategy to speed up the convergence rate is introduced to solve the resource-constrained project scheduling problem(RCPSP)with the objective of minimizing project duration Activities priorities for scheduling are represented by individual vectors and a senal scheme is utilized to transform the individual-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be *** investigate the performance of the IDE-based approach for the RCPSP,it is compared against the meta-heuristic methods of hybrid genetic algorithm(HGA),particle swarm optimization(PSO) and several well selected *** results show that the proposed scheduling method is better than general heuristic rules and is able to obtain the same optimal result as the HGA and PSO approaches but more efficient than the two algorithms.
In this paper, an improved differentialevolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern sy...
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In this paper, an improved differentialevolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm's performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement.
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