Purpose - The purpose of this paper is to compare the performance of the robot arm motion generated by neural controllers in simulated and real robot experiments. Design/methodology/approach - The arm motion generatio...
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Purpose - The purpose of this paper is to compare the performance of the robot arm motion generated by neural controllers in simulated and real robot experiments. Design/methodology/approach - The arm motion generation is formulated as an optimization problem. The neural controllers generate the robot arm motion in dynamic environments optimizing three different objective functions;minimum execution time, minimum distance and minimum acceleration. In addition, the robot motion generation in the presence of obstacles is also considered. Findings - The robot is able to adapt its arm motion generation based on the specific task, reaching the goal position in simulated and experimental tests. The same neural controller can be employed to generate the robot motion for a wide range of initial and goal positions. Research limitations/implications - The motion generated yield good results in both simulation and experimental environments. Practical implications - The robot motion is generated based on three different objective functions that are simultaneously optimized. Therefore, the humanoid robot can perform a wide range of tasks in real-life environments, by selecting the appropriate motion. Originality/value - A new method for adaptive arm motion generation of a mobile humanoid robot operating in dynamic human and industrial environments.
Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node sp...
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Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. evolutionaryalgorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionaryalgorithm in a variety of application domains. (C) 2013 Elsevier Inc. All rights reserved.
This paper presents a modeling framework that intends to select the optimal robust wastewater reclamation program of measures (PoM) to achieve the European Water Framework Directive (WFD) objectives in the inner Catal...
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This paper presents a modeling framework that intends to select the optimal robust wastewater reclamation program of measures (PoM) to achieve the European Water Framework Directive (WFD) objectives in the inner Catalonia watersheds. The integrative methodological tool developed incorporates a water quality model to simulate the effects of the PoM used to reduce pollution pressures on the hydrologic network. A multi-objective evolutionary algorithm (MOEA) helps to identify efficient trade-offs between PoM cost and water quality. Interactive Decisions Map (IDM)-a multi-criteria visualization-based decision support tool is used to provide a clear idea of the trade-off between water status and the cost to achieve such situation. Lastly, a stochastic simulation model to analyze the sensitivity under varied environmental uncertainties is run. Moreover, the tool is oriented to guide water managers in their decision-making processes. Additionally, this paper analyzes the results of the application of the management tool in the inner Catalan watershed in order to perform the European WFD. This tool has had a key role in the design of part of the PoM which shall be implemented to achieve objectives of the WFD in 2015 in all the Catalan catchments.
The optimal rendezvous trajectory designs in many current research efforts do not incorporate the practical uncertainties into the closed loop of the design.A robust optimization design method for a nonlinear rendezvo...
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The optimal rendezvous trajectory designs in many current research efforts do not incorporate the practical uncertainties into the closed loop of the design.A robust optimization design method for a nonlinear rendezvous trajectory with uncertainty is proposed in this *** performance index related to the variances of the terminal state error is termed the robustness performance index,and a two-objective optimization model(including the minimum characteristic velocity and the minimum robustness performance index)is formulated on the basis of the Lambert algorithm.A multi-objective,non-dominated sorting genetic algorithm is employed to obtain the Pareto optimal solution *** is shown that the proposed approach can be used to quickly obtain several inherent principles of the rendezvous trajectory by taking practical errors into ***,this approach can identify the most preferable design space in which a specific solution for the actual application of the rendezvous control should be chosen.
Software testing is a very important part in software projects. As a key issue in software testing, Optimal Testing Resource Allocation Problems (OTRAPs) have drawn more and more attention recently. Along with the rap...
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ISBN:
(纸本)9781467358910
Software testing is a very important part in software projects. As a key issue in software testing, Optimal Testing Resource Allocation Problems (OTRAPs) have drawn more and more attention recently. Along with the rapid increasing of the scale and complexity of software systems, the problems become more and more difficult to solve. Although some single objective optimization approaches had been used to solve such problems, quite a number of flaws were observed with these approaches, such as trapping into local optima, high computational complexity and few available optimal solutions. In this paper, to solve the problem of few available optimal solutions, an effective local search (ELS) is introduced into two effective multi-objective evolutionary algorithms: Nondominated Sorting Genetic algorithm II (NSGA-II) and Harmonic Distance Based multi-objective evolutionary algorithm (HaD-MOEA), advantages of this strategy over pure multi-objective approaches are testified on two OTRAPs with parallel-series modular software systems. To deal with the problem of high computational complexity, the proposed ELS is also embedded into another effective multi-objectivealgorithm, multi-objective evolutionary algorithm based on Decomposition (MOEA/D) to solve OTRAPs. Comprehensive experimental studies show the better performance over the state-of-the-art multi-objective approaches for OTRAPs.
multi-objective Optimization Problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the numb...
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multi-objective Optimization Problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed "Many-objective Optimization Problems (MaOPs)". While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a multi-objective evolutionary algorithm (MOEA) and its MaOP counterpart. (C) 2014 The Authors. Published by Elsevier B.V.
Since multi-objective optimization algorithms (MOEAs) have to find exponentially increasing number of nondominated solutions with the increasing number of objectives, it is necessary to discriminate more meaningful on...
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ISBN:
(纸本)9781479914883
Since multi-objective optimization algorithms (MOEAs) have to find exponentially increasing number of nondominated solutions with the increasing number of objectives, it is necessary to discriminate more meaningful ones from the other nondominated solutions by additionally incorporating user preference into the algorithms. This paper proposes dual multi-objective particle swarm optimization (DMOSPO) by introducing secondary objectives of maximizing both user preference and diversity to the nondominated solutions obtained for primary objectives. The proposed DMOSPO can induce the balanced exploration of the particles in terms of user preference and diversity through the dual-stage of nondominated sorting such that it can generate preferable and diverse nondominated solutions. To demonstrate the effectiveness of the proposed DMOPSO, empirical comparisons with other state-of-the-art algorithms are carried out for benchmark functions. Experimental results show that DMOPSO is competitive with the other compared algorithms and properly reflects the user's preference in the optimization process while maintaining the diversity and solution quality.
Facial recognition is a difficult task due to variations in pose and facial expressions, as well as presence of noise and clutter in captured face images. In this work, we address facial recognition by means of compos...
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ISBN:
(纸本)9781628412444
Facial recognition is a difficult task due to variations in pose and facial expressions, as well as presence of noise and clutter in captured face images. In this work, we address facial recognition by means of composite correlation filters designed with multi-objective combinatorial optimization. Given a large set of available face images having variations in pose, gesticulations, and global illumination, a proposed algorithm synthesizes composite correlation filters by optimization of several performance criteria. The resultant filters are able to reliably detect and correctly classify face images of different subjects even when they are corrupted with additive noise and nonhomogeneous illumination. Computer simulation results obtained with the proposed approach are presented and discussed in terms of efficiency in face detection and reliability of facial classification. These results are also compared with those obtained with existing composite filters.
Selecting accurate and simple association rules that efficiently cover all data samples is very important in knowledge discovery. There are several measures to assess accuracy and relations in a rule. This poses a cha...
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ISBN:
(纸本)9781479980215
Selecting accurate and simple association rules that efficiently cover all data samples is very important in knowledge discovery. There are several measures to assess accuracy and relations in a rule. This poses a challenge for researchers to select effective measures. Combining different measures via multi-objective evolutionary algorithms is an effective method to select suitable association rules. Therefore in this paper NSGAII algorithm is employed for rule selection via different combination of existing measures (support, certainty factor, change of support, Yao and Liu's one way support, cosine and lift) as objectives. The contributions of the paper are twofold. Firstly, some existing measures are modified. Secondly, several experiments are done to evaluate the performance of different combinations of measures through NSGA-II. The experimental results show that the combination of certainty factor and square of cosine measures are more effective in rule selection.
Many multi-objective evolutionary algorithms (MOEAs) have been successful in approximating the Pareto Front. However, well-distributed solutions in the objective and decision spaces are still required in many real-lif...
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Many multi-objective evolutionary algorithms (MOEAs) have been successful in approximating the Pareto Front. However, well-distributed solutions in the objective and decision spaces are still required in many real-life applications. In this paper, a novel MOEA is proposed to this problem. Distinct from other MOEAs, the proposed algorithm suggests a framework, which includes two crowding estimation methods, multiple selection methods for mating and search strategies for variation, to improve the MOEA's searching ability, and the diversity of its solutions. The algorithm emphasizes the importance of using the decision space and the objective space diversities. The objective space crowding and decision space crowding distances are designed using different ideas. To produce new individuals, three different types of mating selections and their respective search strategies are constructed for the main population and the two sparse populations, with the help of the two crowding measurements. Finally, based on the experimental tests on 17 unconstrained multi-objective optimization problems, the proposed algorithm is demonstrated to have better results compared to several state-of-the-art MOEAs. A detailed analysis on the effectiveness and robustness of the framework is also presented.
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