Differential Evolution (DE) has been successfully applied to various optimization problems. The performance of DE is affected by algorithm parameters such as a scaling factor F and a crossover rate CR. Many studies ha...
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ISBN:
(纸本)9783319618241;9783319618234
Differential Evolution (DE) has been successfully applied to various optimization problems. The performance of DE is affected by algorithm parameters such as a scaling factor F and a crossover rate CR. Many studies have been done to control the parameters adaptively. One of the most successful studies on controlling the parameters is JADE. In JADE, the values of each parameter are generated according to one probability density function (PDF) which is learned by the values in success cases where the child is better than the parent. However, search performance might be improved by learning multiple PDFs for each parameter based on some characteristics of search points. In this study, search points are divided into plural groups according to the rank of their objective values and the PDFs are learned by parameter values in success cases for each group. The advantage of JADE with the group-based learning is shown by solving thirteen benchmark problems.
Importance of multi-objective optimization problems has been rapidly increasing in the artificial intelligence community. This significant is due to the fact that there is high number of real-world applications having...
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ISBN:
(纸本)9781538619490
Importance of multi-objective optimization problems has been rapidly increasing in the artificial intelligence community. This significant is due to the fact that there is high number of real-world applications having optimization problems that include more than one objective function. As has been evident in the last ten years, the evolutionary algorithms are one of the best choices to solve multi-objective optimization problems. In this paper a set of improved hybrid Memetic evolutionary algorithms are proposed to solve multi-objective optimization problems. The proposed algorithms enhance the performance of NSGA-II algorithm by using different search schemes. Merging a simple and efficient search technique to NSGA-II significantly enhances the convergence ability and speed of the algorithm. To assess the performance of proposed algorithms, three multi-objective test problems are used from ZDT set. Our empirical results in this paper show that the proposed algorithms significantly enhance the NSGA-II algorithm performance in both diversity and convergence.
In this paper, new algorithms for the full decision tree (DT) induction are presented, and various possibilities for their implementation are explored. First, description is given for the novel EFTI (evolutionary Full...
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ISBN:
(纸本)9781538630730
In this paper, new algorithms for the full decision tree (DT) induction are presented, and various possibilities for their implementation are explored. First, description is given for the novel EFTI (evolutionary Full Tree Induction) algorithm, designed in such a way that its implementations can utilize as little hardware resources as possible for the DT induction, as well as to induce as small decision trees as possible, without sacrificing the classification accuracy. Next, the possibility of the hardware acceleration of the EFTI algorithm is explored in form of the hardware co-processor EFTIP (evolutionary Full Tree Induction co-Processor) using the hardware-software (HW/SW) co-design approach. Next, the algorithm for the induction of the DT ensembles, named EEFTI (Ensembles evolutionary Full Tree Induction) is described, that is able to produce DT ensembles which have higher accuracies when compared to the single DTs. Again, the hardware-software (HW/SW) co-design implementation of the EEFTI algorithm is described and the results of the experiments comparing the execution speeds of the different EEFTI algorithm implementations are given.
In this paper a path planning algorithm for the ship collision avoidance is presented. Tested algorithm is used to determine close to optimal ship paths taking into account changing strategy of dynamic obstacles. For ...
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ISBN:
(纸本)9783319606996
In this paper a path planning algorithm for the ship collision avoidance is presented. Tested algorithm is used to determine close to optimal ship paths taking into account changing strategy of dynamic obstacles. For this purpose a path planning problem is defined. A specific structure of the individual path and fitness function is presented. Principle of operation of evolutionary algorithm and based on it dedicated application vEP/N++ is described. Using presented algorithm the simulations on close-to-real sea environment is performed. Tested environment presents the problem of avoiding one static obstacle representing island and two dynamic objects representing strange ships. Obtained results proof that used approach allows to calculate efficient and close-to-optimal path for marine vessel in close-to-real time.
This paper proposes a new algorithm called Khums Optimization Algorithm (KOA) to solve the fixed head hydrothermal scheduling problem incorporating wind energy (HTSIW) uncertainty. In the hydrothermal scheduling (HT) ...
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ISBN:
(纸本)9781538608173
This paper proposes a new algorithm called Khums Optimization Algorithm (KOA) to solve the fixed head hydrothermal scheduling problem incorporating wind energy (HTSIW) uncertainty. In the hydrothermal scheduling (HT) problem, finding the optimal power allocation of the load among the available thermal unit and hydro units for a period of time aiming to minimize the thermal units total fuel cost. There are many constraints in the HT problem such as real power balance, thermal generation unit power limit constraint and hydro generation unit power limit constraint, water discharge rate, spillage discharge rate, starting storage volume and ending storage volume. In this paper, KOA is used to solve the HTSIW problem for two bench mark systems, then a comparison will be done between the results of both algorithms with other algorithms in literature if possible.
Metaheuristics are widely perceived optimization methods that provide optimal solutions to an expansive range of computational problems. This paper provides a survey of the quantum-inspired metaheuristics, which is a ...
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ISBN:
(纸本)9781538618608
Metaheuristics are widely perceived optimization methods that provide optimal solutions to an expansive range of computational problems. This paper provides a survey of the quantum-inspired metaheuristics, which is a successful alternative to the classical approach for solving optimization problems and combines the principles of quantum computing and metaheuristic. The idea of employing the concept of quantum mechanics in classical computers for better working of the metaheuristic methods has been a flourishing area of research since the last few decades. This paper aims to provide details of current state-of-the-art quantum-inspired metaheuristics by explaining their working principles and the applications of it in order to do further research in this field.
Mixed-initiative Procedural Content Generation uses algorithms to assist human designers in the collaborative creation of game content. Different mixed-initiative approaches use different methods to engage with the de...
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ISBN:
(纸本)9781450353199
Mixed-initiative Procedural Content Generation uses algorithms to assist human designers in the collaborative creation of game content. Different mixed-initiative approaches use different methods to engage with the design material while supporting the designer's intentions. However, the designer runs the risk of misunderstanding the system's abilities and how to control them. In order to limit miscommunication during the design process, heuristics could be applied. In this paper we present a mixed-initiative tool for evolving dungeons with the aid of game design patterns as heuristics. The tool, the evolutionary Dungeon Designer, uses a genetic algorithm that searches for levels containing game design patterns on two hierarchical levels of abstraction to express more complex gameplay in the game level. We evaluate the tool through a series of lab experiments and a user study conducted with professional game developers. Our results demonstrate that we are able to control the generation of the different patterns with the aid of design pattern-related input parameters, as well as identifying a number of features a design pattern-based mixed-initiative tool could benefit from.
Given a new dataset for classification in Machine Learning (ML), finding the best classification algorithm and the best configuration of its (hyper)-parameters for that particular dataset is an open issue. The Automat...
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ISBN:
(纸本)9781450349390
Given a new dataset for classification in Machine Learning (ML), finding the best classification algorithm and the best configuration of its (hyper)-parameters for that particular dataset is an open issue. The Automatic ML (Auto-ML) area has emerged to solve this task. With this issue in mind, in this work we are interested in a specific type of classification problem, called multi-label classification (MLC). In MLC, each example in the dataset can be associated to one or more class labels, making the task considerably harder than traditional, single-label classification. In addition, the cost of learning raises due to the higher complexity of the data. Although the literature has proposed some methods to solve the Auto-ML task, those methods address only the traditional, single-label classification problem. By contrast, this work proposes the first method (an evolutionary algorithm) for solving the Auto-ML task in MLC, i.e., the first method for automatically selecting and configuring the best MLC algorithm for a given input dataset. The proposed evolutionary algorithm is evaluated on three MLC datasets, and compared against two baseline methods according to four different multi-label predictive accuracy measures. The results show that the proposed evolutionary algorithm is competitive against the baselines, but there is still room for improvement.
As one of the most popular and successful methods in industrial applications,model predictive control(MPC)has attracted increasing interest in the past two ***,one of open issues in this research filed is how to solve...
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ISBN:
(纸本)9781509046584
As one of the most popular and successful methods in industrial applications,model predictive control(MPC)has attracted increasing interest in the past two ***,one of open issues in this research filed is how to solve the constrained nonlinear optimization problems in *** the perspective of evolutionary algorithm,this paper presents a novel population extremal optimization(PEO) based modified constrained generalized predictive control(CGPC) method called *** key idea behind the proposed CGPC-PEO is using PEO for rolling optimization to minimize the weighted objective function subjecting to a set of *** superiority to other evolutionary algorithms such as genetic algorithm and particle swarm optimization based CGPC is demonstrated by the simulation results on an industrial process plant.
This paper presents a study on optimized control for a magnetically levitated (MAGLEV) suspension system. Unstable magnetically levitated system is modelled and integer order PID (IOPID) and fractional order PID (FOPI...
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This paper presents a study on optimized control for a magnetically levitated (MAGLEV) suspension system. Unstable magnetically levitated system is modelled and integer order PID (IOPID) and fractional order PID (FOPID) controller parameters are evaluated by using both Genetic Algorithm (GA) and Big Bang Big Crunch (BBBC) algorithm. Comparison between BBBC and GA based controllers are done. Responses for variable reference inputs are obtained. Results show that the performance of the BBBC based FOPID controller is better than GA optimized FOPID controller. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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