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Multiple Criteria Decision Making for Swarm Optimization. Applications to Operations Management Models

Автор:   Ahmed E.
мягкая обложка
200 страниц
2010 год
Издательство:   Книга по Требованию
Цена:
2781 руб
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Описание:The proposed technique is based on an intimate understanding of swarms and animal aggregations in an attempt to simulate cognitive thinking of their members. Fitness and diversity characterize the decision criteria that are used to measure the performance of each alternative. The dynamic balance between gregarious and social intolerance behaviors demonstrated by social animals is used to form the swarm and keep its persistence. Levenshtien edit distance is used to measure the distance between agents in the genotype space. The proposed technique is tested using the traveling salesmen (TSP) and quadratic assignment problems (QAP). Decision making alternatives are defined from the set of basic behaviors. Criteria are then standardized by means of fuzzy sets. The overall decision making process is made to promote both fitness and diversity. This book presents a new formulation for the swarm optimization technique as a system of autonomous agents. Results show that the proposed technique outperforms other techniques included in the comparison in almost all the tested problems. Also, a comparison of the proposed technique with the standard genetic algorithm (SGA) shows that comparable results can be obtained. Adaptive responses are reflected on the way agents move inside the problem domain. For this class of optimization, a large set of diverse benchmark problems is used to test the proposed technique. In this work, members of the swarm are represented by agents that enjoy a certain degree of freewill to respond adaptively to changes on the states of their swarm mates. Fuzzified values of criteria are aggregated by the fuzzy ordered weighted average (OWA) to reach a single evaluation function. A comparison of the performance with the simple evolutionary algorithm SEA and many other particle swarm variants is also carried out. A new set of basic behaviors is defined, namely imitation, memory retrieval, momentum, and play. Results and comparisons show that the technique outperforms the traditional particle swarm optimizer (PSO). A multi-criterion decision making process (MCDM) is employed to update positions of swarm members in the problem space. An extension of the proposed technique is also proposed to solve optimization problems with continuous variables.


Orthomol Immun Junior Сила иммунитета на страже Вашего ребенка Orthomol Immun Junior Сила иммунитета на страже Вашего ребенка

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Programming Embedded Systems in C and C++

Here's how a task's state is actually defined in ADEOS: enum TaskState { Ready, Running, Waiting }; It is important to note that only the scheduler — the part of the operating system that decides which task to run — can promote a task to the running state. Newly created tasks and tasks that are finished waiting for their external event are placed into the ready state first. The scheduler will then include these new ready tasks in its future decision-making. 8.2.1.2 Task mechanics As an application developer working with ADEOS (or any other operating system), you will need to know how to create and use tasks. Like any other abstract data type, the Task class has its own set of routines to do just that. However, the task interface in ADEOS is simpler than most because you can do nothing but create new Task objects. Once created, an ADEOS task will continue to exist in the system until the associated function returns. Of course, that might not happen at all, but if it does, the task will be deleted automatically by the operating system ...»

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