Fuzzy C-means algorithm (FCM) is the most widely used fuzzy partitioning method for data cluster. The K-means algorithm implements fast, however the result is less accurate clustering. In this paper describes a hybrid...
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ISBN:
(纸本)9781424417230
Fuzzy C-means algorithm (FCM) is the most widely used fuzzy partitioning method for data cluster. The K-means algorithm implements fast, however the result is less accurate clustering. In this paper describes a hybridized clustering approach for image segmentation using particle swarm optimization to improve the classical FCM algorithm. The experimental results show that the hybridized clustering approach can provide better effectiveness on experiments of image segmentation.
Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform well for the particular setting. The...
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ISBN:
(纸本)9783031147142;9783031147135
Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform well for the particular setting. The selection is classically done offline, using openly available information about the problem instance or features that are extracted from the instance during a dedicated feature extraction step. This ignores valuable information that the algorithms accumulate during the optimization process. In this work, we propose an alternative, online algorithm selection scheme which we coin as "per-run" algorithm selection. In our approach, we start the optimization with a default algorithm, and, after a certain number of iterations, extract instance features from the observed trajectory of this initial optimizer to determine whether to switch to another optimizer. We test this approach using the CMA-ES as the default solver, and a portfolio of six different optimizers as potential algorithms to switch to. In contrast to other recent work on online per-run algorithm selection, we warm-start the second optimizer using information accumulated during the first optimization phase. We show that our approach outperforms static per-instance algorithm selection. We also compare two different feature extraction principles, based on exploratory landscape analysis and time series analysis of the internal state variables of the CMA-ES, respectively. We show that a combination of both feature sets provides the most accurate recommendations for our test cases, taken from the BBOB function suite from the COCO platform and the YABBOB suite from the Nevergrad platform.
The immune algorithm is an intelligential and heuristic algorithm which imitates high-evolvement of the organism and complicated immune system. Vehicle scheduling problem (VSP) with soft time windows is a variation of...
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ISBN:
(纸本)9780769536996
The immune algorithm is an intelligential and heuristic algorithm which imitates high-evolvement of the organism and complicated immune system. Vehicle scheduling problem (VSP) with soft time windows is a variation of vehicle scheduling problem in logistics distribution, which is a typical NP-hard problem. The paper describes an improved immune optimization algorithm to solve the VSP, in which a new coding method, the adaptive mechanism of crossover and mutation, and evaluate function are introduced. Simulation results on a VSP problems show that the algorithm is efficient to solve the VSP problem.
In this paper, an efficient method is proposed for short-term load forecasting (STLF) in power systems. The proposed method integrates Generalized Radial Basis Function Network (GRBFN) of Artificial Neural Network (AN...
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ISBN:
(纸本)9781713872344
In this paper, an efficient method is proposed for short-term load forecasting (STLF) in power systems. The proposed method integrates Generalized Radial Basis Function Network (GRBFN) of Artificial Neural Network (ANN) with Autoencoder of pretraining in Deep Neural Network (DNN). GRBFN is an extension of Radial Basis Function Network (RBFN) in a way that the parameters of the Gaussian function are determined by the learning process. Autoencoder plays an important role to reduce the number of input variables, which means the dimensionality reduction due to the feature extraction. The hybrid model of GRBFN and Autoencoder results in DNN to improve forecasting model accuracy. Also, evolutionary Particle Swarm Optimization (EPSO) of evolutionary computation is employed to optimize the parameters of GRBFN. The proposed method is successfully applied to real data of short-term load forecasting. Copyright (c) 2023 The Authors.
This paper proposes a multi-project and multiterm portfolio model through considering the remaining funds in investment. The model is based on a new kind of MeanSemi-covariance theory, which describes the uncertainty ...
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ISBN:
(纸本)9781467359214
This paper proposes a multi-project and multiterm portfolio model through considering the remaining funds in investment. The model is based on a new kind of MeanSemi-covariance theory, which describes the uncertainty of return and risk in investment. Portfolio investment is a multiobjective optimization problem with constraints. Multi-objective evolutionary algorithm (MOEA) with greedy repair strategy is used to deal with the infeasible individuals and makes the investment reasonable. Finally, computer simulation shows that the proposed algorithm can be considered as a viable alternative.
In this paper we introduce a restart-CMA-evolution strategy, where the population size is increased for each restart (IPOP). By increasing the population size the search characteristic becomes more global after each r...
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ISBN:
(纸本)0780393635
In this paper we introduce a restart-CMA-evolution strategy, where the population size is increased for each restart (IPOP). By increasing the population size the search characteristic becomes more global after each restart. The IPOP-CMA-ES is evaluated on the test suit of 25 functions designed for the special session on real-parameter optimization of CEC 2005. Its performance is compared to a local restart strategy with constant small population size. On unimodal functions the performance is similar. On multi-modal functions the local restart strategy significantly outperforms IPOP in 4 test cases whereas IPOP performs significantly better in 29 out of 60 tested cases.
The popularity of real-time on-demand transit as a fast evolving mobility service has paved the way to explore novel solutions for point-to-point transit requests. In addition, strict government regulations on greenho...
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ISBN:
(纸本)9783030227500;9783030227494
The popularity of real-time on-demand transit as a fast evolving mobility service has paved the way to explore novel solutions for point-to-point transit requests. In addition, strict government regulations on greenhouse gas emission calls for energy efficient transit solutions. To this end, we propose an on-demand public transit system using a fleet of heterogeneous electric vehicles, which provides real-time service to passengers by linking a zone to a predetermined rapid transit node. Subsequently, we model the problem using a Genetic Algorithm, which generates routes and schedules in real-time while minimizing passenger travel time. Experiments performed using a real map show that the proposed algorithm not only generates near-optimal results but also advances the state-of-the-art at a marginal cost of computation time.
An algorithm based on evolutionary programming (EP) is developed and presented for large numbers of target-weapon assignment. An optimal assignment scheduling in one, which allocates target to weapon such that the tot...
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ISBN:
(纸本)9788890372452
An algorithm based on evolutionary programming (EP) is developed and presented for large numbers of target-weapon assignment. An optimal assignment scheduling in one, which allocates target to weapon such that the total expected of target surviving the defense, is minimized. The proposed method improves EP with reordered mutation operator to handle a large-scale assignment problem. The main advantage of this approach is that the computation time can be controlled via tradeoff performance between the computation time and target surviving value.
Embedded systems often have to calculate some mathematical functions using iterative algorithms. When hard constraints are specified in terms of the area on the chip a possible solution is to implement the iterative a...
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ISBN:
(纸本)9781467358699
Embedded systems often have to calculate some mathematical functions using iterative algorithms. When hard constraints are specified in terms of the area on the chip a possible solution is to implement the iterative algorithm by means of a microprogrammed digital circuit. In this paper, the first version of a new design framework is presented to automate the design and optimization of such microprogrammed systems. The framework utilizes evolutionary design and optimization techniques to find the most suitable implementation of the hardware architecture as well as the program for the programmable logic controller. The functionality of the proposed approach is evaluated using evolutionary design of three HW/SW systems under different constraints.
Hanabi is a cooperative card game with hidden information that has won important awards in the industry and received some recent academic attention. A two-track competition of agents for the game will take place in th...
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ISBN:
(纸本)9781538643594
Hanabi is a cooperative card game with hidden information that has won important awards in the industry and received some recent academic attention. A two-track competition of agents for the game will take place in the 2018 CIG conference. In this paper, we develop a genetic algorithm that builds rule-based agents by determining the best sequence of rules from a fixed rule set to use as strategy. In three separate experiments, we remove human assumptions regarding the ordering of rules, add new, more expressive rules to the rule set and independently evolve agents specialized at specific game sizes. As result, we achieve scores superior to previously published research for the mirror and mixed evaluation of agents.
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