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About 10,000 papers for “Metaheuristic”

A novel metaheuristic optimization algorithm: the monarchy metaheuristic

Ibtissam AHMIA, Méziane Aı̈der · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · 2019

In this paper, we introduce a novel metaheuristic optimization algorithm named the monarchy metaheuristic (MN). Our proposed metaheuristic was inspired by the monarchy government system. Unlike many other metaheuristics, it is easy to implement and does not need a lot of parameters. This makes it applicable to a wide range of optimization problems. To evaluate the efficiency of the proposed algorithm, we examined it on the traveling salesman problem (TSP) using some benchmark from TSPLIB online library of instances for the TSP. The experimental results indicate that the monarchy metaheuristic is competitive with the other methods that exist in the literature for finding approximate solutions.

Cited by 9Open access

Metaheuristic Design Pattern

Alexander E. I. Brownlee, John R. Woodward, Jerry Swan · 2015

Certain problems have characteristics that present difficulties for metaheuristics: their objective function may be either prohibitively expensive, or they may only give a partial ordering over the solutions, lacking a suitable gradient to guide the search. In such cases, it may be more efficient to use a surrogate fitness function to replace or supplement the objective function. This paper provides a broad perspective on surrogate fitness functions, described in the form of a metaheuristic design pattern.

Cited by 20

Metaheuristic Optimization Algorithms

Davut İzci, Putra Sumarı, Mohammad El-Bashır et al. · Bursa Uludag University - AVESIS · 2024

<span style="margin: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, &quot;Times New Roman&quot;, Times, STIXGeneral, &quot;Cambria Math&quot;, &quot;Lucida Sans Unicode&quot;, &quot;Microsoft Sans Serif&quot;, &quot;Segoe UI Symbol&quot;, &quot;Arial Unicode MS&quot;, serif, sans-serif; font-size: 16px;">The moth flame optimization (MFO) algorithm, which is inspired by nature, is studied in this study. This optimizer takes its main inspiration from the directional navigation approach used by moths in nature.&nbsp;<a href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/psychodinae" title="Learn more about Moths fly from ScienceDirect's AI-generated Topic Pages" class="topic-link" style="margin: 0px; padding: 0px; word-break: break-word; text-decoration-line: underline; text-decoration-thickness: 1px; text-decoration-color: rgb(31, 31, 31); color: rgb(31, 31, 31); text-underline-offset: 1px;">Moths fly</a>&nbsp;at a steady angle to the moon at night. This is a particularly efficient method for traveling long distances in a&nbsp;</span><a href="https://www.sciencedirect.com/topics/engineering/straight-line" title="Learn more about straight line from ScienceDirect's AI-generated Topic Pages" class="topic-link" style="margin: 0px; padding: 0px; word-break: break-word; text-decoration-line: underline; text-decoration-thickness: 1px; text-decoration-color: rgb(31, 31, 31); color: rgb(31, 31, 31); text-underline-offset: 1px; font-family: ElsevierGulliver, Georgia, &quot;Times New Roman&quot;, Times, STIXGeneral, &quot;Cambria Math&quot;, &quot;Lucida Sans Unicode&quot;, &quot;Microsoft Sans Serif&quot;, &quot;Segoe UI Symbol&quot;, &quot;Arial Unicode MS&quot;, serif, sans-serif; font-size: 16px;">straight line</a><span style="color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, &quot;Times New Roman&quot;, Times, STIXGeneral, &quot;Cambria Math&quot;, &quot;Lucida Sans Unicode&quot;, &quot;Microsoft Sans Serif&quot;, &quot;Segoe UI Symbol&quot;, &quot;Arial Unicode MS&quot;, serif, sans-serif; font-size: 16px;">. Insects, on the other hand, are trapped in a useless/deadly spiral path around artificial lights. This chapter provides a review of published papers that used the MFO method.</span>

Cited by 0

Metaheuristic Optimization Tool

Ilham Variansyah, Jin Whan Bae, Benjamin R. Betzler et al. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2020

The metaheuristic optimization tool (MOT) is a generic python module for solving a real value, multiobjective, multidimensional optimization problem. Users design their optimization problem by defining search variables, simulations, evaluation metric functions, and constraints. Search variables and constraints are straightforwardly added via python object methods, while simulations and metric functions used for search variable set evaluation must be implemented as a python class. Final optimum result(s) are provided in a tabular text file, as well as search history if needed.

Cited by 3Open access

Metaheuristic Class Libraries

Andréas Fink, Stefan Voß, David L. Woodruff · Kluwer Academic Publishers eBooks · 2006

Just as metaheuristics provide flexible and extensible methods of solving optimization problems, class libraries provide flexible and extensible building blocks for creating software to implement metaheuristics. In this chapter we provide a brief introduction to the class libraries and object oriented programming techniques. We also provide a short summary of some of the more general metaheuristics class libraries and the literature that describes them. We conclude with a detailed example of library design.

Cited by 2

Metaheuristic Design Pattern

Hamzeh Aljawawdeh, Christopher L. Simons, Mohammed Odeh · 2015

In interactive metaheuristic search, the human helps to steer the trajectory of the search by providing qualitative evaluation to assist in the selection of solution individuals. It can be challenging to design mechanisms to exploit human qualitative evaluation taking into account human trade-off judgments among competing criteria and implicit knowledge and experience. This paper addresses this challenge by proposing the Preference metaheuristic design pattern involving six abstractions across two dimensions: explicit and implicit knowledge and a priori, interactive and a posteriori timings.

Cited by 9

Metaheuristic Optimization Methods

Singiresu S. Rao · 2019

A Metaheuristic optimization method can be considered as a more comprehensive intuitive method for the solution of optimization problems. Metaheuristic Optimization Methods developed in recent years can also be considered as metaphor-based optimization methods. All the metaheuristic methods are based on the use of random numbers (probabilistic approaches) in the various stages of the optimization process. Whereas the convergence of the methods such as genetic algorithms, simulated annealing and ant colony optimization has been established, the convergence of most of the recently developed metaheuristic optimization methods does not appear to have been established at the present time. In fact, some researchers think that although the names of the methods might be different, the fundamental ideas used are the same in most of the recently developed metaheuristic optimization methods. The chapter lists the metaphors associated with several of the recently developed metaheuristic optimization methods.

Cited by 2

Metaheuristic design pattern

Krzysztof Krawiec · 2014

In metaheuristic algorithms applied to certain problems, it may be difficult to design search operators that guarantee producing feasible search points. In such cases, it may be more efficient to allow a search operator to yield an infeasible solution, and then turn it into a feasible one using a repair process. This paper is an attempt to provide a broad perspective on the candidate solution repair and frame it as a metaheuristic design pattern.

Cited by 4