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What is Meta-Heuristic

Chapter 4
Handbook of Research on Computational Simulation and Modeling in Engineering
Is a higher-level procedure or heuristic designed to find, generate, or select a lower-level procedure or 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. Meta-heuristic may make few assumptions about the optimization problem being solved, and so they may be usable for a variety of problems.
Published in Chapter:
Multi-Objective Optimization of Two-Stage Thermo-Electric Cooler Using Differential Evolution: MO Optimization of TEC Using DE
Doan V. K. Khanh (Universiti Teknologi PETRONAS, Malaysia), Pandian Vasant (Universiti Teknologi PETRONAS, Malaysia), Irraivan Elamvazuthi (Universiti Teknologi PETRONAS, Malaysia), and Vo N. Dieu (HCMC University of Technology, Vietnam)
Copyright: © 2016 |Pages: 32
DOI: 10.4018/978-1-4666-8823-0.ch004
Abstract
In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. A input current to hot side and cold side of and the number ratio between the hot stage and cold stage are searched the optima solutions. Thermal resistance is taken into consideration. The results of optimization obtained by using differential evolution were validated by comparing with those obtained by using genetic algorithm and show better performance in terms of stability, computational efficiency, robustness. This work revealed that differential evolution more stable than genetic algorithm and the Pareto front obtained from multi-objective optimization balances the important role between cooling rate and coefficient of performance.
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More Results
Chapter 9
Downlink and Uplink Resource Allocation in LTE Networks
is an approach used to find solutions for optimization problems. Meta-heuristic algorithms do not assure to find the optimal solution every time they run. However, they may provide a sufficiently good solution for the problem.
Published in Chapter: Downlink and Uplink Resource Allocation in LTE Networks; From: Handbook of Research on Next Generation Mobile Communication Systems
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Chapter 7
Population-Based vs. Single Point Search Meta-Heuristics for a PID Controller Tuning
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Chapter 17
Application of Standard Deviation Method Integrated PSO Approach in Optimization of Manufacturing Process Parameters
Defined as an iterative generation process which steers a subordinate heuristic by combining intelligently different concepts for exploring and exploiting the search space, learning strategies are used to structure information in order to find efficiently near-optimal solutions.
Published in Chapter: Application of Standard Deviation Method Integrated PSO Approach in Optimization of Manufacturing Process Parameters; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 9
Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification
A meta-heuristic is a generic or higher-level heuristic that is more general in problem-solving. Meta-heuristic computing is adaptive computing that applies general heuristic rules in solving a category of computational problems.
Published in Chapter: Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification; From: Mobile Devices and Smart Gadgets in Medical Sciences
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Chapter 4
Artificial Intelligence and Optimisation Techniques for Risk Reduction in Civil Engineering
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