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OCEANS Hampton Roads Conference

作者： Tucker, Jeff Chavali, Vaibhav Wage, Kathleen E. Nelson, Jill K. George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

Conventional active sonar systems rely on the operator to decide how to best allocate resources to detect and track targets. This may place impractical demands on the operator, particularly when multiple competing objectives are present. A fully adaptive active sonar learns the dynamics of an environment by looking for information on signals of interest and then adapts the system parameters to achieve objectives specified by the user. Traditionally in active sonar, detection and tracking of targets are two common objectives that are carried out non-simultaneously. This paper demonstrates an operator-free simplified intelligent active sonar system capable of simultaneous detection and tracking by allocating pings using a Reinforcement Learning (RL) algorithm. The system uses the popular state-action-reward-state-action RL agent combined with a modified reward metric inspired by multiple objective optimization. Initial evaluation of the approach shows that the intelligent active sonar can swiftly adapt to changes in the dynamic environment when tracking and detecting targets.

关键词： Intelligent active sonar cognitive sonar SARSA multiple objective optimization

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multiple objective optimization of LED Lighting System Design Using Genetic Algorithm 5

Multiple Objective Optimization of LED Lighting System Desig...

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5th International Conference on Information and Communication Technology (ICoIC7)

作者： Santiago, Robert Martin C. Jose, John Anthony Bandala, Argel A. Dadios, Elmer P. De La Salle Univ Elect & Commun Engn Dept Manila Philippines De La Salle Univ Mfg Engn & Management Dept Manila Philippines

ISBN: (纸本)9781509049127

In order to maximize the advantages of LED lighting systems for controlled environment agriculture (CEA), several considerations must be taken into account such as the achievement of required daily light integral (DLI), uniform light distribution over the plant growing area, and minimize the investment and operating costs associated with the lighting system. This study aims to apply the multiple objective optimization of genetic algorithm in designing a lighting system that meets the mentioned objectives. The optimization variables, number of bits per variable and maximum number of iterations are fixed parameters tuned to the requirements of this application and the population size, mutation rate, and selection rate are genetic parameters for explorations. Results of the algorithm suggest the use of a number of LED lamps that is 31.25% lower than the maximum number of lamps that may be used in the plant growing area and, consequently, reduce the investment and operating costs while maintaining the required light integral capacity and uniformity. This and other studies that aim to develop and optimize LED lighting systems open more possibilities and promote the technology for controlled environment. Moreover, control and optimization of agricultural practices can lead to better plant quality and production even on locations and periods that they do not usually grow.

关键词： evolutionary algorithm genetic algorithm lighting system multiple objective optimization

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Satisfying optimization method based on goal programming for fuzzy multiple objective optimization problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2009年第2期197卷 675-684页

作者： Li, Shaoyuan Hu, Chaofang Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China SMERI Shanghai 200031 Peoples R China

This paper proposes a satisfying optimization method based on goal programming for fuzzy multiple objective optimization problem. The aim of this presented approach is to make the more important objective achieving the higher desirable satisfying degree. For different fuzzy relations and fuzzy importance, the reformulated optimization models based on goal programming is proposed. Not only the satisfying results Of all the objectives can be acquired, but also the fuzzy importance requirement can be simultaneously actualized. The balance between optimization and relative importance is realized. We demonstrate the efficiency, flexibility and sensitivity of the proposed method by numerical examples. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Goal programming multiple objective optimization Relative importance Satisfying optimization

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An Interactive Satisfying Method Based on Alternative Tolerance for multiple objective optimization With Fuzzy Parameters

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2008年第5期16卷 1151-1160页

作者： Li, Shaoyuan Hu, Chaofang Shanghai Jiao Tong Univ Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Automat Shanghai 200240 Peoples R China SMERI Shanghai 200031 Peoples R China

An interactive satisfying method based on alternative tolerance is presented for the multiple objective optimization problem with fuzzy parameters. Using the a-level sets of the fuzzy numbers, all the objectives are modeled as the fuzzy goals, and the tolerances of the objectives are iteratively changed according to a decision maker for a satisfying solution. Via a specific attainable point programming model, the membership functions can be modified, and then, a lexicographic two-phase programming procedure is constructed correspondingly to find the final solution. In a special case, the objective constraint is added instead of changing the membership functions;therefore, the dissatisfying objectives for the decision maker can be improved step by step. The presented method not only acquires the alpha-Pareto optimal or weak a-Pareto optimal solution of the fuzzy multiple objective optimization, but also satisfies the progressive preference of the decision maker. A numerical example shows its power.

关键词： alpha-level set fuzzy programming interactive multiple objective optimization

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A landscape lake flow pattern design approach based on automated CFD simulation and parallel multiple objective optimization

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WATER SCIENCE AND TECHNOLOGY 2016年第5期74卷 1155-1162页

作者： Guo, Hao Tian, Yimei Shen, Hailiang Wang, Yi Kang, Mengxin Tianjin Univ Sch Environm Sci & Engn Tianjin 300072 Peoples R China GHD 651 Colby Dr Waterloo ON N2V 1C2 Canada Univ Guelph Guelph ON N1G2W Canada

A design approach for determining the optimal flow pattern in a landscape lake is proposed based on FLUENT simulation, multiple objective optimization, and parallel computing. This paper formulates the design into a multi-objective optimization problem, with lake circulation effects and operation cost as two objectives, and solves the optimization problem with non-dominated sorting genetic algorithm II. The lake flow pattern is modelled in FLUENT. The parallelization aims at multiple FLUENT instance runs, which is different from the FLUENT internal parallel solver. This approach: (1) proposes lake flow pattern metrics, i.e. weighted average water flow velocity, water volume percentage of low flow velocity, and variance of flow velocity, (2) defines user defined functions for boundary setting, objective and constraints calculation, and (3) parallels the execution of multiple FLUENT instances runs to significantly reduce the optimization wall-clock time. The proposed approach is demonstrated through a case study for Meijiang Lake in Tianjin, China.

关键词： computational fluid dynamics (CFD) flow pattern landscape lake multiple objective optimization NSGA-II parallel computing

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A taboo search based approach to find the Pareto optimal set in multiple objective optimization

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ENGINEERING optimization 1999年第6期31卷 731-748页

作者： Baykasoglu, A Owen, S Gindy, N Univ Nottingham Dept Mfg Engn & Operat Management Nottingham NG7 2RD England Univ Gaziantep Dept Ind Engn TR-27310 Gaziantep Turkey

Taboo search is a heuristic optimization technique which works with a neighbourhood of solutions to optimize a given objective function. It is generally applied to single objective optimization problems. Taboo search has the potential for solving multiple objective optimization (MOO) problems, because it works with more than one solution at a time, and this gives it the opportunity to evaluate multiple objective functions simultaneously. In this paper, a taboo search based algorithm is developed to find Pareto optimal solutions in multiple objective optimization problems. The developed algorithm has been tested with a number of problems and compared with other techniques. Results obtained from this work have proved that a taboo search based algorithm can find Pareto optimal solutions in MOO effectively.

关键词： Pareto optimality taboo search multiple objective optimization

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EMOSOR: Evolutionary multiple objective optimization guided by interactive stochastic ordinal regression

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COMPUTERS & OPERATIONS RESEARCH 2019年 108卷 134-154页

作者： Tomczyk, Michal K. Kadzinski, Milosz Poznan Univ Tech Inst Comp Sci Piotrowo 2 PL-60965 Poznan Poland

We propose a family of algorithms, called EMOSOR, combining Evolutionary multiple objective optimization with Stochastic Ordinal Regression. The proposed methods ask the Decision Maker (DM) to holistically compare, at regular intervals, a pair of solutions, and use the Monte Carlo simulation to construct a set of preference model instances compatible with such indirect and incomplete information. The specific variants of EMOSOR are distinguished by the following three aspects. Firstly, they make use of two different preference models, i.e., either an additive value function or a Chebyshev function. Secondly, they aggregate the acceptability indices derived from the stochastic analysis in various ways, and use thus constructed indicators or relations to sort the solutions obtained in each generation. Thirdly, they incorporate different active learning strategies for selecting pairs of solutions to be critically judged by the DM. The extensive computational experiments performed on a set of benchmark optimization problems reveal that EMOSOR is able to bias an evolutionary search towards a part of the Pareto front being the most relevant to the DM, outperforming in this regard the state-of-the-art interactive evolutionary hybrids. Moreover, we demonstrate that the performance of EMOSOR improves in case the forms of a preference model used by the method and the DM's value system align. Furthermore, we discuss how vastly incorporation of different indicators based on the stochastic acceptability indices influences the quality of both the best constructed solution and an entire population. Finally, we demonstrate that our novel questioning strategies allow to reduce a number of interactions with the DM until a high-quality solution is constructed or, alternatively, to discover a better solution after the same number of interactions. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： multiple objective optimization Interactive evolutionary hybrids Stochastic ordinal regression Preference disaggregation Pairwise comparisons Active learning

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An interactive satisficing method based on alternative tolerance for fuzzy multiple objective optimization

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APPLIED MATHEMATICAL MODELLING 2009年第4期33卷 1886-1893页

作者： Hu, Chaofang Shen, Yuling Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

An interactive satisficing method based on alternative tolerance is proposed for fuzzy multiple objective optimization. The new tolerances of the dissatisficing objectives are generated using an auxiliary programming problem. According to the alternative tolerant limits, either the membership functions are changed, or the objective constraints are added. The lexicographic two-phase programming is implemented to find the final solution. The results of the dissatisficing objectives are iteratively improved. The presented method not only acquires the efficient or weak efficient solution of all the objectives, but also satisfies the progressive preference of decision maker. Numerical examples show its power. Crown Copyright (C) 2008 Published by Elsevier Inc. All rights reserved.

关键词： multiple objective decision making multiple objective optimization Interactive Fuzzy programming

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Two-step interactive satisfactory method for fuzzy multiple objective optimization with preemptive priorities

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2007年第3期15卷 417-425页

作者： Li, Shaoyuan Hu, Chaofang Shanghai Jiao Tong Univ Inst Automat Shanghai 200240 Peoples R China

This paper presents a two-step interactive satisfactory optimization method for fuzzy multiple objective optimization with preemptive priorities. In contrast to previous works, this proposed approach guarantees the order of satisfactory degrees consistent with priorities. The decision-maker not only acquires satisfactory solution of all the objectives, but also realizes the preemptive priority requirement among them. The originally complex optimization problem is simplified and divided into two subproblems that are solved in sequence. Numerical examples and actual application show the proposed method's effectiveness, flexibility and efficiency in comparison with results from the literature.

关键词： fuzzy programming fuzzy sets multiple objective decision making (MODM) multiple objective optimization

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Enhanced interactive satisfying optimization approach to multiple objective optimization with preemptive priorities

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2006年第1期5卷 47-63页

作者： Hu, C Li, S Shanghai Jiao Tong Univ Inst Automat Shanghai 200240 Peoples R China

This paper proposes an enhanced interactive satisfying optimization method based on goal programming for the multiple objective optimization problem with preemptive priorities. Based on the previous method, the approach presented makes the higher priority achieve the higher satisfying degree. For three fuzzy relations of the objective functions, the corresponding optimization models are proposed. Not only can satisfying results for all the objectives be acquired, but the preemptive priority requirement can also be simultaneously actualized. The balance between optimization and priorities is realized. We demonstrate the power of this proposed method by illustrative examples.

关键词： goal programming multiple objective optimization preemptive priorities interactive satisfying optimization.

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