Robot path planning is an important content in the field of robot research. Robot Path Planning is a typical multi-objective optimization problem. The path length, the degree of path smoothness and the degree of secur...
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ISBN:
(纸本)9781510830363
Robot path planning is an important content in the field of robot research. Robot Path Planning is a typical multi-objective optimization problem. The path length, the degree of path smoothness and the degree of security are the optimization objectives in this work. And an improved multi-objective PSO method is used for optimization. In this method, in order to make the particle population multi-objective particle swarm optimization algorithm can quickly converge to the Pareto optimal boundary, an environment selection and a matching selection strategy are proposed. At each iteration of the algorithm, in order to improve the population information exchange and reduce the randomness, the environmental selection and matching selection strategy of SPEA2 are used for multi-objective PSO method, and the particle population can faster convergence to the Pareto optimal boundary. The simulation results verify the method, and the result of proposed method is better than that of multi-objective PSO method, and the simulations indicates that the proposed model is practical for robot path planning.
Software requirements selection is the engineering process in which the set of new requirements which will be included in the next release of a software product are chosen. This NP-hard problem is an important issue i...
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Software requirements selection is the engineering process in which the set of new requirements which will be included in the next release of a software product are chosen. This NP-hard problem is an important issue involving several contradictory objectives that have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to cover the highest number of customer requirements. Furthermore, in real instances of the problem, the requirements tackled suffer interactions and other restrictions which complicate the problem. In this paper, we use an adapted multi-objective version of the differential evolution (DE) evolutionaryalgorithm which has been successfully applied to several real instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer's satisfaction, and with three interaction constraints. On the other hand, the original DE algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out to demonstrate the effectiveness of the multiobjective proposal and the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature under a set of public datasets. (c) 2014 Elsevier Inc. All rights reserved.
Software systems are widely employed in society. With a limited amount of testing resource available, testing resource allocation among components of a software system becomes an important issue. Most existing researc...
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Software systems are widely employed in society. With a limited amount of testing resource available, testing resource allocation among components of a software system becomes an important issue. Most existing research on the testing resource allocation problem takes a single-objective optimization approach, which may not adequately address all the concerns in the decision-making process. In this paper, an architecture-based multi-objective optimization approach to testing resource allocation is proposed. An architecture-based model is used for system reliability assessment, which has the advantage of explicitly considering system architecture over the reliability block diagram (RBD)-based models, and has good flexibility to different architectural alternatives and component changes. A system cost modeling approach which is based on well-developed software cost models is proposed, which would be a more flexible, suitable approach to the cost modeling of software than the approach adopted by others which is based on an empirical cost model. A multi-objective optimization model is developed for the testing resource allocation problem, in which the three major concerns in the testing resource allocation problem, i.e., system reliability, system cost, and the total amount of testing resource consumed, are taken into consideration. A multi-objective evolutionary algorithm (MOEA), called multi-objective differential evolution based on weighted normalized sum (WNS-MODE), is developed. Experimental studies are presented, and the experiments show several results. 1) The proposed architecture-based multi-objective optimization approach can identify the testing resource allocation strategy which has a good trade-off among optimization objectives. 2) The developed WNS-MODE is better than the MOEA developed in recent research, called HaD-MOEA, in terms of both solution quality and computational efficiency. 3) The WNS-MODE seems quite robust from the sensitivity analysis results.
Sustainable water management in a changing environment full of uncertainty is profoundly challenging. To deal with these uncertainties, dynamic adaptive policies that can be changed over time are suggested. This paper...
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Sustainable water management in a changing environment full of uncertainty is profoundly challenging. To deal with these uncertainties, dynamic adaptive policies that can be changed over time are suggested. This paper presents a model-driven approach supporting the development of promising adaptation pathways, and illustrates the approach using a hypothetical case. We use robust optimization over uncertainties related to climate change, land use, cause-effect relations, and policy efficacy, to identify the most promising pathways. For this purpose, we generate an ensemble of possible futures and evaluate candidate pathways over this ensemble using an Integrated Assessment Meta Model. We understand 'most promising' in terms of the robustness of the performance of the candidate pathways on multiple objectives, and use a multi-objective evolutionary algorithm to find the set of most promising pathways. This results in an adaptation map showing the set of most promising adaptation pathways and options for transferring from one pathway to another. Given the pathways and signposts, decision-makers can make an informed decision on a dynamic adaptive plan in a changing environment that is able to achieve their intended objectives despite the myriad of uncertainties.
A large number of techniques for data analyses have been developed in recent years, however most of them do not deal satisfactorily with a ubiquitous problem in the area: the missing data. In order to mitigate the bia...
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A large number of techniques for data analyses have been developed in recent years, however most of them do not deal satisfactorily with a ubiquitous problem in the area: the missing data. In order to mitigate the bias imposed by this problem, several treatment methods have been proposed, highlighting the data imputation methods, which can be viewed as an optimization problem where the goal is to reduce the bias caused by the absence of information. Although most imputation methods are restricted to one type of variable whether categorical or continuous. To fill these gaps, this paper presents the multi-objective genetic algorithm for data imputation called MOGAImp, based on the NSGA-II, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and the modeling task. A set of tests for evaluating the performance of the algorithm were applied using 30 datasets with induced missing values;five classifiers divided into three classes: rule induction learning, lazy learning and approximate models;and were compared with three techniques presented in the literature. The results obtained confirm the MOGAImp outperforms some well-established missing data treatment methods. Furthermore, the proposed method proved to be flexible since it is possible to adapt it to different application domains. (C) 2015 Elsevier B.V. All rights reserved.
Portfolio optimization problem is a multi-objective optimization problem, it is necessary to consider the benefits should also consider the risks and the optimal situation is to achieve the least risk and maximum retu...
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ISBN:
(纸本)9781510835429
Portfolio optimization problem is a multi-objective optimization problem, it is necessary to consider the benefits should also consider the risks and the optimal situation is to achieve the least risk and maximum return. Papers define portfolio optimization problems, analyze the shortcomings of the existing portfolio model and the corresponding solution ideas, introduce multi-objective genetic algorithm and demonstrate the feasibility of multi-objective genetic algorithm in the application portfolio. algorithm design process, detailed process of constructing and building a portfolio investment in line with the actual situation, put forward five objective optimization model from all angles, through a reasonable, optimized simulation test, and then select the algorithm selected from a number of programs better overall performance of the program.
In order to improve the thermodynamic efficiency of an internal combustion engine (ICE), a Stephenson-III six-bar linkage is optimized to serve as a replacement for the traditional slider-crank. Novel techniques are p...
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In order to improve the thermodynamic efficiency of an internal combustion engine (ICE), a Stephenson-III six-bar linkage is optimized to serve as a replacement for the traditional slider-crank. Novel techniques are presented for formulating the design variables in the kinematic optimization that guarantee satisfaction of the Grashof condition and of transmission angle requirements without the need for an explicit constraint function. Additionally, a nested generalization of the popular NSGA-II algorithm is presented that allows simultaneous optimization of the kinematic, dynamic, and thermodynamic properties of the mechanism. This approach successfully solves the complex six-objective optimization problem, with challenges for future refinement including improvement of the combustion simulation to attain better accuracy without prohibitive computational expense.
In this paper, a new proportion entropy function is proposed as an objective function to obtain a well-diversified portfolio. Secondly, a new fuzzy multi-objective portfolio selection model based on the proposed entro...
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ISBN:
(纸本)9781467386609
In this paper, a new proportion entropy function is proposed as an objective function to obtain a well-diversified portfolio. Secondly, a new fuzzy multi-objective portfolio selection model based on the proposed entropy function is presented. By using the model, we can find tradeoffs between risk, return and the diversification degree of portfolio. Thirdly, a new multi-objective evolutionary algorithm is designed to solve the proposed model. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed model and the corresponding algorithm.
Determining the contribution of an agent to a system-level objective function (credit assignment) is a key area of research in cooperative multiagent systems. multi-objective optimization is a growing area of research...
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ISBN:
(纸本)9781450334136
Determining the contribution of an agent to a system-level objective function (credit assignment) is a key area of research in cooperative multiagent systems. multi-objective optimization is a growing area of research, though mostly focused on single agent settings. Many real-world problems are multiagent and multi-objective, (e.g., air traffic management, scheduling observations across multiple exploration robots) yet there is little work on their intersection. In this work, we leverage recent advances in single-objectivemultiagent learning to address multi-objective domains. We focus on the impact of difference evaluation functions (which extracts an agent's contribution to the team objective) on the Non-dominated Sorting Genetic algorithm-II (NSGA-II), a state-of-the-art multi-objective evolutionary algorithm. We derive multiple methods for incorporating difference evaluations into the NSGA-II framework, and test each in a multiagent rover exploration domain, which is a good surrogate for a wide variety of distributed scheduling and resource gathering problems. We show that how and where difference evaluations are incorporated in the NSGA-II algorithm is critical, and can either provide significant benefits or destroy system performance, depending on how it is used. Median performance of the correctly used difference evaluations dominates best-case performance of NSGA-II in a multiagent multi-objective problem.
Deceptive fitness landscapes are a growing concern for the field of evolutionary computation. Recent work has demonstrated that incorporating human insights with short-term automated evolution has a synergistic effect...
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ISBN:
(纸本)9781509002870
Deceptive fitness landscapes are a growing concern for the field of evolutionary computation. Recent work has demonstrated that incorporating human insights with short-term automated evolution has a synergistic effect that eases deception and accelerates the discovery of solutions. While human evaluators provide rich insight into a domain, they fatigue easily. Previous work reduced the number of human evaluations by evolving a diverse set of candidates via intermittent searches for novelty. While successful at evolving solutions for a deceptive maze domain, it lacked the ability to measure solution qualities that the human evaluator implicitly identified as important. The key insights of this paper are that multi-objective evolutionary algorithms (MOEAs) foster diversity and that the non-dominated set can serve as a surrogate for novelty while measuring user preferences data. This new approach, called Pareto Optimality-Assisted Interactive evolutionary Computation (POA-IEC), leverages human intuitions by allowing users to identify candidates in the non-dominated set that they feel are promising. Interestingly, the experimental results demonstrate that POA-IEC finds solutions in significantly fewer evaluations than previous approaches, and that the non-dominated set contains significantly more novel behaviors than the dominated set. In this way, POA-IEC simultaneously leverages human insights while quantifying their preferences.
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