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What is Metaheuristic

Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems
A top-level general strategy which can be adapted to search for feasible solutions in domains where the task is hard.
Published in Chapter:
Scheduling in Flexible Manufacturing Systems: Genetic Algorithms Approach
Fraj Naifar (Digital Research Center of Sfax, Tunisia), Mariem Gzara (University of Monastir, Tunisia), and Taicir Loukil Moalla (Tabuk University, Saudi Arabia)
DOI: 10.4018/978-1-5225-2944-6.ch001
Abstract
Flexible manufacturing systems have many advantages like adaptation to changes and reduction of lateness. But flexible machines are expensive. The scheduling is a central functionality in manufacturing systems. Optimizing the job routing through the system, while taking advantage from the flexibility of the machines, aims at improving the system's profitability. The introduction of the flexibility defines a variant of the scheduling problems known as flexible job shop scheduling. This variant is more difficult than the classical job shop since two sub-problems are to be solved the assignment and the routing. To guarantee the generation of efficient schedules in reasonable computation time, the metaheuristic approach is largely explored. Particularly, much research has addressed the resolution of the flexible job shop problem by genetic algorithms. This chapter presents the different adaptations of the genetic scheme to the flexible job shop problem. The solution encodings and the genetic operators are presented and illustrated by examples.
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More Results
A Fuzzy Simulated Evolution Algorithm for Hard Problems
An iterative computational algorithm that optimizes a hard problem from a single or population of candidate solutions.
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A Memetic Algorithm for Integrated Production Distribution Problem in a Supply Chain
A high-level procedure or heuristic to solve complicated optimization problems.
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Cuckoo Search Algorithm
A computational method to find optimal solutions by iteratively improving candidate solutions under a pre-determined measure of quality or time.
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Evaluation of Genetic Algorithm as Learning System in Rigid Space Interpretation
A meta-heuristic is a heuristic approach of solving a general class of computational problem by combining heuristics themselves in a hopefully efficient way.
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Different Approaches for Cooperation with Metaheuristics
A high-level strategy for solving a very general class of computational problems by combining user given black-box procedures usually heuristics in a hopefully efficient way.
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Prey Predator Algorithm for Travelling Salesman Problem: Application on the Ethiopian Tourism Sites
A higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.
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Managing Transportation in Supply Chain: Metaheuristics for Solving a Capacitated Fixed-Charge Transportation Problem
An approach that can provide near optimal solutions within an acceptable time for large-scale optimization problems.
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Hybrid Genetic Metaheuristic for Two-Dimensional Constrained Guillotinable Cutting Problems
A computational method that optimizes a problem by iteratively trying to improve a candidate solution among very large spaces of candidate solutions with regard to a given measure of quality.
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Swarm Bee Colony Optimization for Heat Exchanger Distributed Dynamics Approximation With Application to Leak Detection
A higher-level procedure or heuristic designed to find, generate, or select a heuristic that may provide a sufficiently good solution to an optimization problem.
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Metaheuristics Approaches to Solve the Employee Bus Routing Problem With Clustering-Based Bus Stop Selection
A master strategy that guides and modifies other heuristics to produce solutions beyond those that are normally generated in a quest for local optimality.
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Cuckoo Search for Optimization and Computational Intelligence
A class of stochastic algorithms which combines deterministic and stochastic components. They are often developed by drawing inspiration from Nature.
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Metaheuristic Design Patterns: New Perspectives for Larger-Scale Search Architectures
A higher-level technique exploiting heuristics to arrive at a sufficiently satisfactory solution to an optimization problem, especially with limited information or finite computational capacity. In contrast to iterative methods such as exhaustive search, metaheuristics do not guarantee that a globally optimum solution can be arrived at for a given class of problems.
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Artificial Bee Colony Algorithm
A computational method to find optimal solutions by iteratively improving candidate solutions under a pre-determined measure of quality.
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Data Clustering Using Sine Cosine Algorithm: Data Clustering Using SCA
A general algorithmic framework which can be applied to different optimization problems with relatively few modifications to make them adapted to a specific problem.
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