This article compares three multiobjective evolutionary algorithms (MOEAs) with application to the urban drainage system (UDS) adaptation of a capital city in North China. Particularly, we consider the well-known NSGA...
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This article compares three multiobjective evolutionary algorithms (MOEAs) with application to the urban drainage system (UDS) adaptation of a capital city in North China. Particularly, we consider the well-known NSGA-II, the built-in solver in the MATLAB Global Optimization Toolbox (MLOT), and a newly-developed hybrid MOEA called GALAXY. A variety of parameter combinations of each MOEA is systemically applied to examine their impacts on optimization efficiency. Results suggest that the traditional MOEAs suffer from severe parameterization issues. For NSGA-II, the distribution indexes of crossover and mutation operators were found to have dominant impacts, while the probabilities of the two operators played a secondary role. For MLOT, the two-point and the scattered crossover operators accompanied by the adaptive-feasible mutation operator gained the best Pareto fronts, provided the crossover fraction is set to lower values. In contrast, GALAXY was the most robust and easy-to-use tool among the three MOEAs, owing to its elimination of various associated parameters of searching operators for substantially alleviating the parameterization issues. This study contributes to the literature by showing how to improve the robustness of identifying optimal solutions through better selection of operators and associated parameter settings for real-world UDS applications.
Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of d...
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Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP's practical application. Alternatively, this study focuses on the use of evolutionarymultiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa Binh water reservoir in Vietnam, where water operators are seeking to balance the conflicting objectives of maximizing hydropower production and minimizing flood risks. A key choice in the EMODPS approach is the selection of alternative formulations for flexibly representing reservoir operating policies. This study distinguishes between the relative performance of two widely-used nonlinear approximating networks, namely artificial neural networks (ANNs) and radial basis functions (RBFs). The results show that RBF solutions are more effective than ANN ones in designing Pareto approximate policies for the Hoa Binh reservoir. Given the approximate nature of EMODPS, the diagnostic benchmarking uses SDP to evaluate the overall quality of the attained Pareto-approximate results. Although the Hoa Binh test case's relative simplicity should maximize the potential value of SDP, the results demonstrate that EMODPS successfully dominates the solutions derived via SDP. (C) 2015 America
This paper studies a multi-objective instance of the university exam timetabling problem. On top of satisfying universal hard constraints such as seating capacity and no overlapping exams, the solution to this problem...
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ISBN:
(纸本)9781424407040
This paper studies a multi-objective instance of the university exam timetabling problem. On top of satisfying universal hard constraints such as seating capacity and no overlapping exams, the solution to this problem requires the minimization of the timetable length as well as the number of occurrences of students having to take exams in consecutive periods within the same day. While most existing approaches to the problem, as well as the more popular single-objective instance, require prior knowledge of the desired timetable length, the multi-objective evolutionaryalgorithm proposed in this paper is able to generate feasible solutions even without the information. The effectiveness of the proposed algorithm is benchmarked against a few recent and established optimization techniques and is found to perform well in comparison.
Mobile agents are often used in wireless sensor networks for distributed target detection with the goal of minimizing the transmission of non-critical data that negatively affects the performance of the network. A cha...
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ISBN:
(纸本)9781424481262
Mobile agents are often used in wireless sensor networks for distributed target detection with the goal of minimizing the transmission of non-critical data that negatively affects the performance of the network. A challenge is to find optimal mobile agent routes for minimizing the data path loss and the sensors energy consumption as well as maximizing the data accuracy. Existing approaches deal with the objectives individually, or by optimizing one and constraining the others or by combining them into a single objective. This often results in missing "good" tradeoff solutions. Only few approaches have tackled the Mobile Agent-based Distributed Sensor Network Routing problem as a multiobjective Optimization Problem (MOP) using conventional Multi-Objective evolutionaryalgorithms (MOEAs). It is well known that the incorporation of problem specific knowledge in MOEAs is a difficult task. In this paper, we propose a problem-specific MOEA based on Decomposition (MOEA/D) for optimizing the three objectives. Experimental studies have shown that the proposed problem-specific approach performs better than two conventional MOEAs in several WSN test instances.
In this paper we introduce an Efficient Multi-Objective evolutionaryalgorithm (EMOEA) to design circuits. The algorithm is based on non-dominated set for keeping diversity of the population and therefore, avoids trap...
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ISBN:
(纸本)0769526144
In this paper we introduce an Efficient Multi-Objective evolutionaryalgorithm (EMOEA) to design circuits. The algorithm is based on non-dominated set for keeping diversity of the population and therefore, avoids trapping in local optimal. Encoding of the chromosome is based on J. F. Miller's implementation[1], but we use efficient methods to evaluate and evolve circuits for speeding up the convergence of the algorithm. This algorithm evolves complex combinational circuits (such as 3-bit multiplier and 4 bit full adder) without too much long time evolution (commonly less than 5, 000, 000).
The pickup and delivery problem (PDP) arises in many real-world scenarios such as logistics and robotics. This problem combines the traveling salesman problem (or the vehicle routing problem) and object distribution. ...
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ISBN:
(纸本)9781467359054
The pickup and delivery problem (PDP) arises in many real-world scenarios such as logistics and robotics. This problem combines the traveling salesman problem (or the vehicle routing problem) and object distribution. The selective pickup and delivery problem (SPDP) is a novel variant of the PDP that enables selectivity of pickup nodes for particular applications. Specifically, the SPDP seeks a shortest route that can supply all delivery nodes with required commodities from some pickup nodes. The two key factors in the SPDP travel distance and vehicle capacity required form a tradeoff in essence. This study formulates the biobjective selective pickup and delivery problem (BSPDP) for minimization of travel distance and vehicle capacity required. To resolve the BSPDP, we propose a multiobjective memetic algorithm (MOMA) based on NSGA-II and local search. Furthermore, a repair strategy is developed for the MOMA to handle the constraint on vehicle load. Experimental results validate the efficacy of the proposed algorithm in approaching the lower bounds of both objectives. Moreover, the results demonstrate the characteristics of the BSPDP.
Mining is an important industry in Australia, contributing billions of dollars to the economy. The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant design deci...
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ISBN:
(纸本)0780393635
Mining is an important industry in Australia, contributing billions of dollars to the economy. The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant design decisions are typically guided more by intuition and experience than by analysis. In this paper, we motivate the use of an evolutionaryalgorithm to aid in the design of such plants. We formalise plant design in terms suitable for application in a multi-objective evolutionaryalgorithm and create a simulation to assess the performance of candidate solutions. Results show the effectiveness of this approach with our algorithm producing designs superior to those used in practice today, promising significant financial benefits.
This paper deals with the application of a heuristic-based evolutionaryalgorithms (EAs) on a specific industrial problem-the scheduling of rapid transit systems. The system under consideration is a medium-sized mass ...
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ISBN:
(纸本)0780393635
This paper deals with the application of a heuristic-based evolutionaryalgorithms (EAs) on a specific industrial problem-the scheduling of rapid transit systems. The system under consideration is a medium-sized mass rapid transit (MRT) system and the dual objectives of minimizing operating costs and passenger dissatisfaction are considered. Making use of concepts such as Pareto-optimality and multiobjective evolutionary algorithms, the authors applied a multiobjective evolutionary algorithm (MOEA) to solve the problem. Comparison studies were done with current method, obtaining satisfactory results.
In this paper we propose a multi-objective evolutionaryalgorithm with a mechanism to improve the interpretability in the sense of complexity for Linguistic Fuzzy Rule based Systems with adaptive defuzzification. The ...
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ISBN:
(纸本)9781424469208
In this paper we propose a multi-objective evolutionaryalgorithm with a mechanism to improve the interpretability in the sense of complexity for Linguistic Fuzzy Rule based Systems with adaptive defuzzification. The use of parameters in the defuzzification operator introduces a series of values or associated weights to each rule, which improves the accuracy but increases the system complexity and therefore has an effect on the system interpretability. To this end, we use maximizing the accuracy as an unusual objective for the evolutionary process, and we defined objectives related with interpretability, using three metrics: minimizing the classical number of rules, the number of rules, with weights associated and the average number of rules triggered by each example. The proposed method was compared in an experimental study with a single objective accuracy-guided algorithm in two real problems showing that many solutions in the Pareto front dominate those obtained by the single objective-based one.
A supply chain network system is to provide an optimal platform for efficient and effective supply chain management. There's increasingly competitive, multi-channel retail world calls for a radically new strategy ...
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ISBN:
(纸本)9783642172977
A supply chain network system is to provide an optimal platform for efficient and effective supply chain management. There's increasingly competitive, multi-channel retail world calls for a radically new strategy for evaluating supply chain network design. Retailers must abandon past practices which look to optimize the number and placement of facilities within traditional networks. A multi-objective optimization procedure which permits a trade-off evaluation for an integrated model is initially presented. This model includes elements of total cost, customer service and flexibility as its objectives and integrates facility location and inventory control decisions. Inventory control issues include economic order quantity, safety stock and inventory replenishment decisions and consider the risk pooling phenomenon to be realized from collaborative initiatives such as vendor-managed inventory. The possibility of a multi-objective evolutionary approach is developed to determine the optimal facility location portfolio and is implemented on a real large retail supply chain in Taiwan to investigate the model performance. Some preliminary results are described.
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