作者：
Bistarelli, S.Gennari, R.Rossi, F.Università di Pisa
Dipartimento di Informatica Corso Italia 40 Pisa56125 Italy ILLC
Institute of Logic Language and Computation University of Amsterdam N. Doelenstraat 15 Amsterdam1012 CP Netherlands Università di Padova
Dipartimento di Matematica Pura ed Applicata Via Belzoni 7 Padova35131 Italy
Soft constraints based on semirings are a generalization of classical constraints, where tuples of variables’ values in each soft constraint are uniquely associated to elements from an algebraic structure called semi...
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the constraint satisfaction problem (CSP) is a central generic problem in artificial intelligence. Considerable effort has been made in identifying properties which ensure tractability in such problems. In this paper ...
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Arc consistency plays a central role in solving constraint Satisfaction Problems. this is the reason why many algorithms have been proposed to establish it. Recently, an algorithm called AC2001 and AC3.1 has been inde...
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this paper proposes a new binary particle swarm optimization (BPSO) approach inspired from quantum computing, so-called quantum-inspired BPSO (QBPSO), for solving the unit commitment (UC) problems. Although BPSO-based...
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this paper proposes a new binary particle swarm optimization (BPSO) approach inspired from quantum computing, so-called quantum-inspired BPSO (QBPSO), for solving the unit commitment (UC) problems. Although BPSO-based approaches have been successfully applied to the combinatorial optimization problems of power systems, the BPSO algorithm has some drawbacks such as premature convergence when handling heavily constrained problems. the proposed QBPSO combines the conventional BPSO withthe concept and principles of quantum computing such as a quantum bit and superposition of states. the QBPSO adopts a Q-bit individual for the probabilistic representation, which replaces the velocity update procedure in the particle swarm optimization. this paper also proposes an efficient rotation gate for updating Q-bit individuals to improve the searching capability of the quantum computing. To verify the performance of the proposed QBPSO, it is applied to the test systems of up to 100-units with 24-hour demand horizon.
For practical reasons, most scheduling problems are an abstraction of the real problem being solved. For example, when you plan your day, you schedule the activities which are critical;that is you schedule the activit...
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ISBN:
(纸本)3540292381
For practical reasons, most scheduling problems are an abstraction of the real problem being solved. For example, when you plan your day, you schedule the activities which are critical;that is you schedule the activities which are essential to the success of your day. So you may plan what time to leave the house to get to work, when to have meetings, how you share your vehicle with your spouse and so on. On the other hand, you probably do not consider the activities that are easy to arrange like brushing your teeth, going to the shops, making photocopies and other such tasks that can usually be accomplished whenever you have the time available. Scheduling all of these activities at once is often too complicated. Instead, a simpler schedule is produced by considering only the critical activities. However, if a schedule goes wrong, it is often because an activity turned out to be critical but was not scheduled. We typically learn which activities are critical by experience and create an abstract scheduling problem which includes all known critical activities. Instead of scheduling the non-critical activities we estimate their effects in the abstract scheduling problem. We are interested in automating this abstraction process for scheduling problems. In our approach, given a set of activities A to be scheduled1, we choose a subset of activities, critical(A), and create a simplified scheduling model which approximates the other activities non-critical(A) instead of scheduling them. We then search this abstract model for a good, if not optimal solution. A solution is a partial order schedule for activities in critical(A). this abstract solution is then extended to a solution the entire problem by inserting the remaining activities non-critical(A) into the schedule. While the approach reduces complexity by solving the problem in two stages it does so at a price. there is a risk that the good abstract solution will not produce a good solution to the entire problem. We know
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