The 0/1 knapsack problem is a well-known problem, which appears in many real domains with practical importance. The problem is NP-complete. The multiobjective 0/1 knapsack problem is a generalization of the 0/1 knapsa...
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The 0/1 knapsack problem is a well-known problem, which appears in many real domains with practical importance. The problem is NP-complete. The multiobjective 0/1 knapsack problem is a generalization of the 0/1 knapsack problem in which multiple knapsacks are considered. Many algorithms have been proposed in the past five decades for both single and multiobjective knapsack problems. A new version of MOEA/D with uniform design for solving multiobjective 0/1 knapsack problems is proposed in this paper. The algorithm adopts the uniform design method to generate the aggregation coefficient vectors so that the decomposed scalar optimization subproblems are uniformly scattered, and therefore the algorithm could explore uniformly the region of interest from the initial iteration. To illustrate how the algorithm works, some numerical experiments on the benchmark multiobjective knapsack problems are realized. Experimental results show that the proposed algorithm outperforms NSGA-II, SPEA2 and PESA significantly for the 2-objective, 3-objective and 4-objective knapsack problems.
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.
The *** Task Parallel Library is the latest tool developed for multicore parallelism optimization using the NET technology. It is a managed concurrency library that provides optimized managed code for multicore proces...
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ISBN:
(纸本)9781905088294
The *** Task Parallel Library is the latest tool developed for multicore parallelism optimization using the NET technology. It is a managed concurrency library that provides optimized managed code for multicore processors using a new thread pool that supports cancellation, waiting and pool isolation, among many other features. The Task Parallel Library also uses dynamic work stealing techniques for superior scalability. In this paper we analyze the performance improvement in a timetable planner implemented with a multiobjective evolutionary algorithm using the Task Parallel Library of ***.
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.
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).
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.
This paper presents a multi-objective optimisation by reinforcement learning, called MORL, to solve complex multi-objective optimisation problems, in particular those in a high-dimensional space. In MORL, the search i...
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ISBN:
(纸本)9781424481262
This paper presents a multi-objective optimisation by reinforcement learning, called MORL, to solve complex multi-objective optimisation problems, in particular those in a high-dimensional space. In MORL, the search is undertaken on individual dimension in a high-dimensional space via a path selected by an estimated path value. Path values, estimated by weighting the state values on the selected path, represent the potentiality of finding a better solution if searching on the paths, and are used to memorize the quality of previously visited states. In MORL, visited states are assigned with different immediate rewards by comparing the objective vector of current state with those of the Pareto optimal solutions found previously. These Pareto optimal solutions are stored in an elite list, which keeps track of the non-dominated solutions found so far and is used to construct the Pareto front at the end of the optimisation process. MORL is compared with a promising multi-objective evolutionaryalgorithm based on decomposition (MOEA/D) on four widely-used benchmark functions. The simulation results have demonstrated that MORL is superior over MOEA/D with respect to the accuracy and the range of the Pareto fronts, especially in solving high-dimensional multi-objective optimisation problems.
Machining parameters optimization is very crucial in any machining process. This research focuses on Multi-objective evolutionaryalgorithm based optimization technique, to determine optimal cutting parameters (cuttin...
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ISBN:
(纸本)9781424481262
Machining parameters optimization is very crucial in any machining process. This research focuses on Multi-objective evolutionaryalgorithm based optimization technique, to determine optimal cutting parameters (cutting speed, feed, and depth of cut) in turning operation. Two conflicting objectives (operation time and tool life) with three constraints, which depends on the turning parameters, are optimized using Genetic algorithm (GAs). The Pareto-optimal front of the bi-objective problem is obtained using Non-dominated Sorting Genetic algorithm (NSGA-II). The extreme and intermediate points of Pareto optimal front is verified using Real coded Genetic algorithm (RGA) as well as epsilon-constraint method. The performance of NSGA-II is found to be more effective and efficient as compared to micro-GA. Innovization study carried out to correlate cutting parameters with the aforementioned objective functions. The effect of cutting speed is found more as compared to feed rate and depth of cut.
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