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检索条件"主题词=evolutionary computation"

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Multi-Objective Optimization in Asynchronous Environments: An evolutionary Game Theory Approach 32

Multi-Objective Optimization in Asynchronous Environments: A...

引用

32nd International Conference on Modeling, Analysis and Simulation of Computer and Telecommunication Systems

作者： Jarosz, Pawel Marszalek, Adam Cracow Univ Technol Fac Comp Sci & Telecommun Krakow Poland

ISBN: (纸本)9798331531317;9798331531300

This paper presents an innovative approach to solving complex multi-objective optimization problems through an asynchronous and distributed evolutionary game theory method. The proposed algorithm, an extension of the IMGAMO algorithm, optimizes individual criteria separately at varying computational speeds, thus significantly enhancing computational efficiency and adaptability. This unique structure enables independent criterion optimization, catering to real-world applications where different objectives demand varying computational resources. The algorithm's effectiveness is validated against traditional synchronous evolutionary multi-objective optimization algorithms, showing superior performance in handling diverse, real-world problems efficiently. The results underline the potential of the asynchronous approach in providing high-quality Pareto fronts, thus offering robust solutions for complex optimization challenges.

关键词： Multiobjective optimization Pareto optimization evolutionary computation Asynchronous computation

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Stochastic Optimal Allocation of PMUs for Improving the Accuracy of State Estimation

Stochastic Optimal Allocation of PMUs for Improving the Accu...

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IEEE Symposium Series on computational Intelligence (IEEE SSCI)

作者： Mori, H. Ogawa, S. Chiang, H. D. Meiji Univ Dept Network Design Nakano Ku Tokyo 1618525 Japan Cornell Univ Sch Elect & Comp Engn Ithaca NY 14850 USA

ISBN: (纸本)9781538627266

In this paper, a new method is proposed for the optimal allocation of Phasor Measurement Units (PMUs) in power system state estimation. They play an important role to provide more accurate measurements with state estimation. This paper focuses on the improvement of the estimates accuracy with them. In practice, it is of main concern how to optimize the allocation for a set of power system conditions. The proposed method makes use of evolutionary Particle Swarm Optimization (EPSO) to determine the optimal allocation of PMUs. In practice, the optimal allocation is dependent upon the power system conditions. To overcome the problem, this paper introduces Monte Carlo Simulation (MCS) in consideration of the nodal correlation. Specifically, Moment Matching Method is used to evaluate the optimal allocation efficiently. The effectiveness of the proposed method is demonstrated in the IEEE 30-node power system.

关键词： Power Systems State Estimation PMU Optimization evolutionary computation Monte Carlo Simulation Moment Matching Method

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A Binary Differential Evolution with Adaptive Parameters Applied to the Multiple Knapsack Problem 13

A Binary Differential Evolution with Adaptive Parameters App...

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13th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Andre, Leanderson Parpinelli, Rafael Stubs Univ Estado Santa Catarina Dept Comp Sci Grad Program Appl Comp Joinville Brazil

ISBN: (纸本)9783319136509;9783319136493

This paper introduces an adaptive Binary Differential Evolution (aBDE) that self adjusts two parameters of the algorithm: perturbation and mutation rates. The well-known 0-1 Multiple Knapsack Problem (MKP) is addressed to validate the performance of the method. The MKP is a NP-hard optimization problem and the aim is to maximize the total profit subjected to the total weight in each knapsack that must be less than or equal to a given limit. Results were obtained using 11 instances of the problem with different degrees of complexity. The results were compared using aBDE, BDE, a standard Genetic Algorithm (GA), and its adaptive version (aGA). The results show that aBDE obtained better results than the other algorithms. This indicates that the proposed approach is an interesting and promising strategy for control of parameters and for optimization of complex problems.

关键词： Adaptive Parameter Control Binary Differential Evolution Multiple Knapsack Problem evolutionary computation

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On the Definition of Dynamic Permutation Problems under Landscape Rotation 19

On the Definition of Dynamic Permutation Problems under Land...

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Genetic and evolutionary computation Conference (GECCO)

作者： Alza, Joan Bartlett, Mark Ceberio, Josu McCall, John Robert Gordon Univ Aberdeen Scotland Univ Basque Country UPV EHU Donostia San Sebastian Spain

ISBN: (纸本)9781450367486

Dynamic optimisation problems (DOPs) are optimisation problems that change over time. Typically, DOPs have been defined as a sequence of static problems, and the dynamism has been inserted into existing static problems using different techniques. In the case of dynamic permutation problems, this process has been usually done by the rotation of the landscape. This technique modifies the encoding of the problem and maintains its structure over time. Commonly, the changes are performed based on the previous state, recreating a concatenated changing problem. However, despite its simplicity, our intuition is that, in general, the landscape rotation may induce severe changes that lead to problems whose resemblance to the previous state is limited, if not null. Therefore, the problem should not be classified as a DOP, but as a sequence of unrelated problems. In order to test this, we consider the flow shop scheduling problem (FSSP) as a case study and the rotation technique that relabels the encoding of the problem according to a permutation. We compare the performance of two versions of the state-of-the-art algorithm for that problem on a wide experimental study: an adaptive version that benefits from the previous knowledge and a restarting version. Conducted experiments confirm our intuition and reveal that, surprisingly, it is preferable to restart the search when the problem changes even for some slight rotations. Consequently, the use of the rotation technique to recreate dynamic permutation problems is revealed in this work.

关键词： Dynamic Optimization Problem Flow Shop Scheduling Problem Permutation Problem Benchmark Generator evolutionary computation

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A Self-Adaptive Strategy for Controlling Parameters in Differential Evolution

A Self-Adaptive Strategy for Controlling Parameters in Diffe...

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IEEE Congress on evolutionary computation

作者： Soliman, Omar S. Bui, Lam T. Univ New S Wales Australian Def Force Acad Sch ITEE Canberra ACT 2600 Australia

ISBN: (纸本)9781424418220

The Differential Evolution (DE) is a stochastic population-based search method for global optimization over continuous spaces. This paper presents an efficient strategy for self-adapting control parameters in Differential Evolution to solve real-parameter optimization problems. The proposed strategy introduces an adaptive mechanism at the individual level based on Cauchy distribution(CD) where the step length and crossover rate are self-adapted during the evolution process. This strategy is to utilize attractive features of CD, which has thick tails that enable it to generate considerable changes more frequently and to escape a local optima for multi-modal optimization problems. Detailed performance comparisons of a DE using the proposed strategy on wide range of fifteen standard benchmark test problems are carried out. The obtained results showed that the performance of the DE had been improved with the proposed self-adaptive strategy.

关键词： Differential Evolution evolutionary computation Self-Adaptive DE Parameters Control and Cauchy Distribution

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evolutionary Algorithms and Reinforcement Learning in Experiments with Slot Cars

Evolutionary Algorithms and Reinforcement Learning in Experi...

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International Conference on Process Control (PC)

作者： Martinec, Dan Bundzel, Marek Czech Tech Univ Fac Elect Engn Dept Control Engn Prague 16627 Czech Republic

ISBN: (纸本)9781479909261;9781479909278

Some control systems are difficult or impossible to be tuned by other means than automatically. We present here examples of optimization of the parameters of a PID controller regulating velocity of a slot car to the given set point using evolutionary optimization and reinforcement learning. These methods are implemented on the micro-controller of the slot car. Experimental results and comparison are provided.

关键词： automobiles control engineering computing evolutionary computation learning (artificial intelligence) microcontrollers road traffic control three-term control velocity control

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Multi-objective Mobile Agent-based Sensor Network Routing using MOEA/D

Multi-objective Mobile Agent-based Sensor Network Routing us...

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2010 IEEE World Congress on computational Intelligence

作者： Konstantinidis, Andreas Charalambous, Christoforos Zhou, Aimin Zhang, Qingfu Frederick Univ Dept Comp Sci & Engn Nicosia Cyprus East China Normal Univ Dept Comp Sci & Technol Shanghai Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester Essex CO4 3SQ England

ISBN: (纸本)9781424481262

Mobile agents are often used in wireless sensor networks for distributed target detection with the goal of minimizing the transmission of non-critical data that negatively affects the performance of the network. A challenge is to find optimal mobile agent routes for minimizing the data path loss and the sensors energy consumption as well as maximizing the data accuracy. Existing approaches deal with the objectives individually, or by optimizing one and constraining the others or by combining them into a single objective. This often results in missing "good" tradeoff solutions. Only few approaches have tackled the Mobile Agent-based Distributed Sensor Network Routing problem as a Multiobjective Optimization Problem (MOP) using conventional Multi-Objective evolutionary Algorithms (MOEAs). It is well known that the incorporation of problem specific knowledge in MOEAs is a difficult task. In this paper, we propose a problem-specific MOEA based on Decomposition (MOEA/D) for optimizing the three objectives. Experimental studies have shown that the proposed problem-specific approach performs better than two conventional MOEAs in several WSN test instances.

关键词： MOEA/D WSN data communication data path loss minimization decomposition based MOEA distributed target detection energy consumption evolutionary computation mobile agent based sensor network mobile agents multiobjective evolutionary algorithm multiobjective optimization problem network routing network routing object detection wireless sensor network wireless sensor networks

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Vehicle Relocation Problem in Free Floating Carsharing Using Multiple Shuttles

Vehicle Relocation Problem in Free Floating Carsharing Using...

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IEEE Congress on evolutionary computation (CEC)

作者： Santos, Andre G. Candido, Paulo G. L. Balardino, Allan F. Herbawi, Wesam Univ Fed Vicosa Informat Dept Vicosa MG Brazil Moovel Lab Stuttgart Germany

ISBN: (纸本)9781509046010

We present an Integer Linear Programming (ILP) formulation and an evolutionary Algorithm (EA) to solve the vehicle relocation problem in free floating carsharing. In this system, users rent cars and may leave them anywhere on a designated area. After a while, a set of vehicles must be relocated to a discrete set of weighted spots, where other users may rent them again. In order to do it, some shuttles drive a set of operators to vehicles to be relocated and then collect the operators back. The objective is to maximize the weighted sum of served spots on a given time. The ILP model could solve the small instances and gave an upper (UB) and a lower bound (LB) for the others. The UB and LB values were used to evaluate the solution found by EA and showed that EA indeed found good solutions, even optimal ones.

关键词： evolutionary computation Integer linear programming Electronic mail Upper bound Informatics Automobiles

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A Novel Particle Swarm Optimization Algorithm with Local Search for Dynamic Constrained Multi-objective Optimization Problems

A Novel Particle Swarm Optimization Algorithm with Local Sea...

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IEEE Congress on evolutionary computation

作者： Wei, Jingxuan Jia, Liping Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (纸本)9781479904549;9781479904532

In the real world, many optimization problems are dynamic constrained multi-objective optimization problems. This requires an optimization algorithm not only to find the global optimal solutions under a specific environment but also to track the trajectory of the varying optima over dynamic environments. To address this requirement, this paper proposes a novel particle swarm optimization algorithm for such problems. This algorithm employs a new points selection strategy to speed up evolutionary process, and a local search operator to search optimal solutions in a promising subregion. The new algorithm is examined and compared with two well-known algorithms on a sequence of benchmark functions. The results show that the proposed algorithm can effectively track the varying Pareto fronts over time. The proposed developments are effective individually, but the combined effect is much better for the test functions.

关键词： Pareto optimisation dynamic programming evolutionary computation particle swarm optimisation search problems

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A Multi-Objective evolutionary Approach to Imbalanced Classification Problems 11

A Multi-Objective Evolutionary Approach to Imbalanced Classi...

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11th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Chira, Camelia Lemnaru, Camelia Tech Univ Cluj Napoca Dept Comp Sci Cluj Napoca 400027 Romania

ISBN: (纸本)9781467382007

Classification problems for imbalanced data distribution pose many challenges to standard learning algorithms as at least one class is under-represented relative to others. In this paper, we present a new approach to deal with this kind of problems, in which a multi-objective evolutionary algorithm is engaged to detect the best cost matrix to be further used by the learning algorithm in the classification task. Two objectives are set for the evolutionary algorithm as follows: maximize the true positive rate and maximize precision on the minority class. A multi-objective search algorithm is used for this optimization problem and the detected optimal costs are then used in the classifier. Experiments are performed for several imbalanced datasets and the results obtained support a competitive performance of the proposed approach.

关键词： evolutionary computation learning (artificial intelligence) matrix algebra pattern classification cost matrix imbalanced classification problems imbalanced data distribution multiobjective evolutionary approach multiobjective search algorithm standard learning algorithms evolutionary computation Glass Optimization Search problems Sociology Standards Statistics evolutionary algorithm tabu search Sociology matrix algebra Pattern recognition Glass learning algorithms

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