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Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.


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Gilbert Laporte1
01 Oct 2009
TL;DR: The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two of the most popular problems in the field of combinatorial optimization.
Abstract: The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two of the most popular problems in the field of combinatorial optimization. Due to the study of these two problems, there has been a significant growth in families of exact and heuristic algorithms being used today. The purpose of this paper is to show how their study has fostered developments of the most popular algorithms now applied to the solution of combinatorial optimization problems. These include exact algorithms, classical heuristics and metaheuristics.

13 citations

Book ChapterDOI
16 Dec 2010
TL;DR: The proposed SaDE-MMTS is employed to solve the 20 numerical optimization problems for the CEC’2010 Special Session and Competition on Large Scale Global Optimization and competitive results are presented.
Abstract: In order to solve large scale continuous optimization problems, Self-adaptive DE (SaDE) is enhanced by incorporating the JADE mutation strategy and hybridized with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS). The JADE mutation strategy, the “DE/current-to-pbest” which is a variation of the classic “DE/current-to-best”, is used for generating mutant vectors. After the mutation phase, the binomial (uniform) crossover, the exponential crossover as well as no crossover option are used to generate each pair of target and trial vectors. By utilizing the self-adaptation in SaDE, both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, suitable offspring generation strategy along with associated parameter settings will be determined adaptively to match different phases of the search process. MMTS is applied frequently to refine several diversely distributed solutions at different search stages satisfying both the global and the local search requirement. The initialization of step sizes is also defined by a self-adaption during every MMTS step. The success rates of both SaDE and the MMTS are determined and compared, consequently, future function evaluations for both search algorithms are assigned proportionally to their recent past performance. The proposed SaDE-MMTS is employed to solve the 20 numerical optimization problems for the CEC’2010 Special Session and Competition on Large Scale Global Optimization and competitive results are presented.

13 citations

Proceedings ArticleDOI
20 Jul 2016
TL;DR: Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.
Abstract: Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. Copyright is held by the owner/author(s). GECCO’16 Companion, July 20-24, 2016, Denver, CO, USA ACM 978-1-4503-4323-7/16/07. http://dx.doi.org/10.1145/2908961.2926994 Final version

13 citations

Proceedings ArticleDOI
01 Aug 2007
TL;DR: This paper presents a combination of two well-known metaheuristic algorithms, particle swarm optimization (PSO) and ant colony system (ACS), based on a framework design named A-B-Domain, and observes that a guided search through ACS possible sets of parameters obtains better results than the basic ACS with an extended number of trials.
Abstract: This paper presents a combination of two well-known metaheuristic algorithms, particle swarm optimization (PSO) and ant colony system (ACS), based on a framework design named A-B-Domain. We take the travelling salesmanpsilas problem as the benckmark problem. ACPS2, as we name this combination, works as a metaheuristic for the TSP. When considering deviations to lower bounds, ACPS2 shows an improvement over the simple ACS with a high computational cost. Proposed policies are able to reduce, significatively, running times. As a final conclusion we observe that a guided search through ACS possible sets of parameters obtains better results than the basic ACS with an extended number of trials.

13 citations

Proceedings ArticleDOI
01 Jun 2008
TL;DR: An ACO approach to optimal control is proposed, which requires that a continuous-time, continuous-state model of the system, together with a finite action set, is formulated as a discrete, non-deterministic automaton.
Abstract: Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for Combinatorial Optimization Problems (COPs). It has been demonstrated to work well when applied to various NP-complete problems, such as the traveling salesman problem. In this paper, an ACO approach to optimal control is proposed. This approach requires that a continuous-time, continuous-state model of the system, together with a finite action set, is formulated as a discrete, non-deterministic automaton. The control problem is then translated into a stochastic COP. This method is applied to the time-optimal swing-up and stabilization of a pendulum.

13 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20232
202213
20217
20209
201922
201815