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What is Metaheuristics

Chapter 669
Encyclopedia of Information Science and Technology, Fourth Edition
High-level strategies that define algorithms to find approximate solutions for optimization problems.
Published in Chapter:
Efficient Optimization Using Metaheuristics
Sergio Nesmachnow (Universidad de la República, Uruguay)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-2255-3.ch669
Abstract
This chapter provides an insight into the main concepts, theoretical advances, and experimental results in the field of metaheuristics, when applied for efficiently solving real-world optimization problems. A general view of the most well-known metaheuristic methods is presented. After that, relevant applications of metaheuristics in nowadays real-world problems from several domains are described, highlighting on their capabilities to solve complex problems with high efficiency. Finally, the main current and future research lines in the field are also summarized and commented.
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More Results
Chapter 67
Metaheuristics: Heuristic Techniques for Combinatorial Optimization Problems
These are strategies that guide a heuristic search process to efficiently explore the search space in order to find a (close to) optimal solution.
Published in Chapter: Metaheuristics: Heuristic Techniques for Combinatorial Optimization Problems; From: Encyclopedia of Decision Making and Decision Support Technologies
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Chapter 119
Harmony Search for Multiple Dam Scheduling
Technique to find solutions by combining black-box procedures (heuristics). Here, ‘meta’ means ‘beyond’, and ‘heuristic’ means ‘to find’
Published in Chapter: Harmony Search for Multiple Dam Scheduling; From: Encyclopedia of Artificial Intelligence
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Chapter 5
Application of Optimization to Sizing Renewable Energy Systems and Energy Management in Microgrids: State of the Art and Trends
A metaheuristic is a higher-level procedure to find, generate, or select a sufficiently good solution to an optimization problem. Metaheuristics require a few assumptions about the optimization problem being solved and so may be usable for a variety of problems.
Published in Chapter: Application of Optimization to Sizing Renewable Energy Systems and Energy Management in Microgrids: State of the Art and Trends; From: Applications of Nature-Inspired Computing in Renewable Energy Systems
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Chapter 14
Comparative Analysis of ACO Algorithms for the Solution of the Travelling Salesman Problem
Are classes of approximate methods that are designed to solve difficult combinatorial optimization problems, in which classical heuristics are neither effective nor efficient.
Published in Chapter: Comparative Analysis of ACO Algorithms for the Solution of the Travelling Salesman Problem; From: Handbook of Research on Software Quality Innovation in Interactive Systems
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Chapter 4
Supply Chain Design Including Quality Considerations: Modeling and Solution Approaches based on Metaheuristics
Heuristic procedures that can be adapted to a wide variety of problems as opposed to ad hoc heuristics that often apply only to one particular problem. They can often be used to address problems that cannot be tackled through traditional optimization theory. Metaheuristics iteratively aim to improve a candidate solution with respect to a given measure of quality (fitness); some metaheuristics use stochastic search methodologies.
Published in Chapter: Supply Chain Design Including Quality Considerations: Modeling and Solution Approaches based on Metaheuristics; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 5
Hub Location Allocation Problems and Solution Algorithms
In computer science and mathematical optimization, a metaheuristic is 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 (Bianchi et al., 2009 AU83: The in-text citation "Bianchi et al., 2009" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Published in Chapter: Hub Location Allocation Problems and Solution Algorithms; From: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems
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Chapter 6
Metaheuristics Methods for Configuration of Assembly Lines: A Survey
Higher level of general methodology that provides guidance based on the heuristic search to solve various optimization problems.
Published in Chapter: Metaheuristics Methods for Configuration of Assembly Lines: A Survey; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 20
Swarm Intelligence for Multiobjective Optimization of Extraction Process
A framework consisting of a class of algorithms employed to find good solutions to optimization problems by iterative improvement of solution quality.
Published in Chapter: Swarm Intelligence for Multiobjective Optimization of Extraction Process; From: Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics
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Chapter 27
The Bees Algorithm as a Biologically Inspired Optimisation Method
A higher-level procedure that manages and guides a lower-level heuristic in search. In the Bees Algorithm, the bees foraging metaheuristics manages the random local and global search heuristics.
Published in Chapter: The Bees Algorithm as a Biologically Inspired Optimisation Method; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 11
Insulin DNA Sequence Classification Using Levy Flight Bat With Back Propagation Algorithm
It is a type of approximate algorithm and is composed of two Latin words meta and heuristic, meta means upper limit and heuristic signifies the art of determining new approaches. It is designed on the map of heuristic and leads to an optimal solution
Published in Chapter: Insulin DNA Sequence Classification Using Levy Flight Bat With Back Propagation Algorithm; From: Mobile Devices and Smart Gadgets in Medical Sciences
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Chapter 5
Variable Selection in Multiple Linear Regression Using a Genetic Algorithm
Optimization methods that search for good solutions of a problem, usually based on iterative steps that approximate the solution; the “meta” term refers to the fact that a basic frame is used to many different situations.
Published in Chapter: Variable Selection in Multiple Linear Regression Using a Genetic Algorithm; From: Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics
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Chapter 82
A Fuzzy Multi-Agent System for Combinatorial Optimization
The name combines the Greek prefix “meta” (“beyond”) and “heuristic.” It is a method for solving a very general class of computational problems by combining heuristics in a hopefully efficient way (Blum, 2003; Melian et al., 2003).
Published in Chapter: A Fuzzy Multi-Agent System for Combinatorial Optimization; From: Encyclopedia of Networked and Virtual Organizations
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Chapter 727
Using Metaheuristics as Soft Computing Techniques for Efficient Optimization
High-level soft computing strategies that define algorithmic techniques to find approximate solutions for optimization problems.
Published in Chapter: Using Metaheuristics as Soft Computing Techniques for Efficient Optimization; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 2
Application and Evaluation of Bee-Based Algorithms in Scheduling: A Case Study on Project Scheduling
A general concept of algorithms which can be applied to different types of optimization problems.
Published in Chapter: Application and Evaluation of Bee-Based Algorithms in Scheduling: A Case Study on Project Scheduling; From: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems
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