Job Shop Scheduling Problem (JSSP) represents a real challenge for the researchers' community due to its complexity consisting in the plurality of resources that needs to be optimally used and the variety of goals...
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ISBN:
(纸本)9781509020478
Job Shop Scheduling Problem (JSSP) represents a real challenge for the researchers' community due to its complexity consisting in the plurality of resources that needs to be optimally used and the variety of goals that needs to be accomplished. This paper presents the implementation of three evolutionary algorithms (Genetic algorithms, Particle Swarm Optimization and Ant Colony Optimization) for the JSSP. The tests are made considered a set of classical benchmarks for the proposed problem and the obtained results are subject to comparison.
We analyze the performance of evolutionary algorithms on various matroid optimization problems that encompass a vast number of efficiently solvable as well as NP-hard combinatorial optimization problems (including man...
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ISBN:
(纸本)9781595936974
We analyze the performance of evolutionary algorithms on various matroid optimization problems that encompass a vast number of efficiently solvable as well as NP-hard combinatorial optimization problems (including many well-known examples such as minimum spanning tree and maximum bipartite matching). We obtain very promising bounds on the expected running time and quality of the computed solution. Our results establish a better theoretical understanding of why randomized search heuristics yield empirically good results for many real-world optimization problems.
The resource constrained project scheduling problem(RCPSP) has received wide attention in the last 20 years with a number of evolutionary algorithms being proposed. Most of these algorithms can produce optimal or near...
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ISBN:
(纸本)9781509025978
The resource constrained project scheduling problem(RCPSP) has received wide attention in the last 20 years with a number of evolutionary algorithms being proposed. Most of these algorithms can produce optimal or near optimal solutions in less than a second. However, a close investigation of the literature will reveal a number of questionable benchmarking practices. In this paper I highlight some of these issues together with possible future research directions which are mainly centred around the use of hyper-heuristics.
Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and ***,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expa...
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Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and ***,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational ***,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables *** paper is dedicated to addressing the challenges posed by *** start,we introduce innovative objective functions customized to mine maximum and minimum candidate *** substantial enhancement dramatically improves the efficacy of frequent pattern *** this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific ***,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero *** novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function *** extensively tested our algorithm across eight benchmark problems and four real-world *** results demonstrate that our approach achieves competitive solutions across various challenges.
This paper presents the potential of genetic programming (GP), an evolutionary computing algorithm, for reducing or eliminating significant second-order linear model LOF by automatically generating appropriate transfo...
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This paper presents the potential of genetic programming (GP), an evolutionary computing algorithm, for reducing or eliminating significant second-order linear model LOF by automatically generating appropriate transformations. A case study in an industrial setting at The Dow Chemical Company will be presented to illustrate this methodology. Lack of fit, transformations, linear regression.
The main goal of this flight control system is to achieve good performance with acceptable flying quality within the specified flight envelope while ensuring robustness for model variations, such as mass variation due...
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Over the last two decades, evolutionary Computation (EC) has shown tremendous success for solving complex real-world problems. Although the great success for EC was first recognized in the 1980s, the researchers in ot...
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Aircraft flight control design is a multivariable control problem with multiple sensors and multiple actuators where various strict requirements from multiple disciplines have to be satisfied. In this paper a method b...
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We apply a hybrid evolutionary algorithm to minimize the depth of circuits in quantum computing. More specifically, we evaluate two different variants of the algorithm. In the first approach, we combine the evolutiona...
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The prediction of electron distributions in semiconductor devices is compulsory for the design of modern computer chips. In spite of increasing computation facilities the calculation of electron distributions at high ...
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