This paper presents an evolutionary quasi-solution for a problem commonly occurring in practical logistics, the three-dimensional version of the bin packing problem. The algorithm presented here is a variation of the ...
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ISBN:
(纸本)9781467315067
This paper presents an evolutionary quasi-solution for a problem commonly occurring in practical logistics, the three-dimensional version of the bin packing problem. The algorithm presented here is a variation of the bacterialevolutionary approach, and utilizes fuzzy logic in the fitness calculation. The goal is to give a useful alternative method to the basic problem, and to demonstrate that the addition of fuzzy logic elements to the fitness function increases the speed of the evolutionary process. The paper first describes the specific problem, then moves on to the details of every key part of the algorithm. Finally, the results from a number of test runs are used to show the general efficiency, and the contrast between the crisp and fuzzy fitness functions. It is clearly shown that the application of fuzzy approach in the fitness function can improve the speed of convergence, so the fuzzy logic can be helpful even in solving crisp problems.
Monitoring trails (m-trails) have been extensively studied as an alternative to the conventional link-based monitoring approach by using multi-hop supervisory lightpaths in all-optical networks. However, none of the p...
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ISBN:
(纸本)9781467320153;9781467320160
Monitoring trails (m-trails) have been extensively studied as an alternative to the conventional link-based monitoring approach by using multi-hop supervisory lightpaths in all-optical networks. However, none of the previous studies have investigated the effect of length constraints upon the m-trail formation, which nonetheless correspond to the failure localization time. This paper addresses the above issue and formulates a new m-trail allocation problem, where the relationship between the number of m-trails versus the maximum hop count is explored. First, the paper investigates the theoretical bounds of allocating m-trails with at most k hops via an optimal group testing construction. Secondly, a novel meta-heuristic approach based on bacterial evolutionary algorithm for solving the length-constrained m-trail allocation problem is introduced. Through extensive simulations the performance gap of the proposed algorithm to the lower bound is presented on a wide diversity of topologies.
This paper presents an evolutionary quasi-solution for a problem commonly occurring in practical logistics, the three-dimensional version of the bin packing problem. The algorithm presented here is a variation of the ...
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ISBN:
(纸本)9781467315074
This paper presents an evolutionary quasi-solution for a problem commonly occurring in practical logistics, the three-dimensional version of the bin packing problem. The algorithm presented here is a variation of the bacterialevolutionary approach, and utilizes fuzzy logic in the fitness calculation. The goal is to give a useful alternative method to the basic problem, and to demonstrate that the addition of fuzzy logic elements to the fitness function increases the speed of the evolutionary process. The paper first describes the specific problem, then moves on to the details of every key part of the algorithm. Finally, the results from a number of test runs are used to show the general efficiency, and the contrast between the crisp and fuzzy fitness functions. It is clearly shown that the application of fuzzy approach in the fitness function can improve the speed of convergence, so the fuzzy logic can be helpful even in solving crisp problems.
In many regression learning algorithms for fuzzy rule bases it is not, possible to define the error measure to be optimized freely. A possible alternative is the usage of global optimization algorithms like genetic pr...
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ISBN:
(纸本)9788073683863
In many regression learning algorithms for fuzzy rule bases it is not, possible to define the error measure to be optimized freely. A possible alternative is the usage of global optimization algorithms like genetic programming approaches. These approaches, however, are very slow because of the high complexity of the search space, In this paper we present a novel approach where we first, create a large set, of (possibly) redundant rules using inductive rule learning and where we use a bacteria] evolutionaryalgorithm to identify the best subset of rules in a subsequent step. The evolutionaryalgorithm tries to find an optimal rule set with respect to a freely definable goal function.
The job-shop scheduling problem is one of the most complicated and well-known hardest combinatorial optimization problems. It's purpose is to improve the production efficiency and reduce the processing duration so...
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The job-shop scheduling problem is one of the most complicated and well-known hardest combinatorial optimization problems. It's purpose is to improve the production efficiency and reduce the processing duration so as to gain profits as high as possible. In addition, it has been illustrated that job-shop scheduling is usually an NP-hard combinatorial problem and is therefore unlikely to be solvable in polynomial time. In this study, a bacterial evolutionary algorithm is proposed for finding multiple optimal solutions to the job-shop scheduling problem. bacterial evolutionary algorithm is an optimization method that incorporates special mechanisms inspired by natural phenomena of microbial evolution. Gene transfer and bacterial mutation operators are incorporated to improve the performance of the proposed method. Moreover, niche scheme is employed to discover multiple solutions. Numerous well-studied benchmark examples were utilized to evaluate the effectiveness of the proposed approach.
For designing and developing products/services it is vital to know the relevancy of the performance generated by each technical attribute and how they can increase customer satisfaction. Improving the parameters of te...
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ISBN:
(纸本)9781424420827
For designing and developing products/services it is vital to know the relevancy of the performance generated by each technical attribute and how they can increase customer satisfaction. Improving the parameters of technical attributes requires financial resources, and the budgets are generally limited. Thus the optimum target is to achieve maximum customer satisfaction within given financial limits. Kano's quality model classifies the relationships between customer satisfaction and attribute-level performance and indicates that some of the attributes have a non-linear relationship to satisfaction, rather power-function should be used. For the customers' subjective evaluation these relationships are not deterministic and are uncertain. This paper proposes a method for fuzzy extension of Kano's model and presents numerical examples that can prove the efficiency of bacterial evolutionary algorithm in as well.
In this paper a new algorithm for the learning of Takagi-Sugeno fuzzy systems is introduced. In the algorithm different learning techniques are applied for the antecedent and the consequent parameters of the fuzzy sys...
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ISBN:
(纸本)9780780394889
In this paper a new algorithm for the learning of Takagi-Sugeno fuzzy systems is introduced. In the algorithm different learning techniques are applied for the antecedent and the consequent parameters of the fuzzy system. We propose a hybrid method for the antecedent parameters learning based on the combination of the bacterial evolutionary algorithm (BEA) and the Levenberg-Marquardt (LM) method. For the linear parameters in fuzzy systems appearing in the rule consequents the Least Squares (LS) and the Recursive Least Squares (RLS) techniques are applied, which will lead to a global optimal solution of linear parameter vectors in the least squares sense. Therefore a better performance can be guaranteed than with a complete learning by BEA and LM. The paper is concluded by evaluation results based on high-dimensional test data. These evaluation results compare the new method with some conventional fuzzy training methods with respect to approximation accuracy and model complexity.
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