نتایج جستجو برای: combinatorial optimization crossover

تعداد نتایج: 380301  

2015
Maricar M. Navarro Bryan B. Navarro

Facility Layout Problem (FLP) is logic based combinatorial optimization problem. It is a meta-heuristic solution approach that gained significant attention to obtained optimal facility layout. This paper examines the convergence analysis by changing the crossover and mutation probability in an optimal facility layout. This algorithm is based on appropriate techniques that include multipoint swa...

Journal: :Adv. Operations Research 2012
Mohammad Jalali Varnamkhasti Lai Soon Lee Mohd Rizam Abu Bakar Wah June Leong

The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for solving binary encoded combinatorial optimization problems. A new crossover operator and probabil...

Journal: :CoRR 2010
Md. Amjad Hossain Md. Kawser Hossain M. M. A. Hashem

This paper proposes a generalized Hybrid Real-coded Quantum Evolutionary Algorithm (HRCQEA) for optimizing complex functions as well as combinatorial optimization. The main idea of HRCQEA is to devise a new technique for mutation and crossover operators. Using the evolutionary equation of PSO a Single-Multiple gene Mutation (SMM) is designed and the concept of Arithmetic Crossover (AC) is used ...

Here, issues connected with characteristic stochastic practices are considered. In the first part, the plausibility of covering the arrangements of an improvement issue on subjective subgraphs is studied. The impulse for this strategy is a state where an advancement issue must be settled as often as possible for discretionary illustrations. Then, a preprocessing stage is considered that would q...

2003
Michael Affenzeller

Most problems of combinatorial optimization like routing, task allocation, or scheduling belong to the class of NP-complete problems and can be solved efficiently only by heuristics. Both, genetic algorithms and evolutionary strategies have a number of drawbacks that reduce their applicability to that kind of problems. In order to overcome some of these problems, this paper looks upon the stand...

2014
Dong-Li Duan Xiao-Dong Ling Xiao-Yue Wu Bin Zhong

Electrical distribution network reconfiguration is a complex combinatorial optimization process aimed at finding a radial operating structure that minimizes the system power loss or/and maximizes the system reliability while satisfying operating constraints. In this paper, a distribution network reconfiguration method is presented for both the indices of power loss reduction and reliability imp...

Journal: :Oberwolfach Reports 2008

Journal: :Journal of physics 2021

Abstract Traveling Salesman Problem (TSP) is a typical combinatorial optimization problem, and it NP-hard problem. The total number of routes increases exponentially with the cities, so great significance to design an effective algorithm find optimal solution accurately. Chicken Swarm Optimization (CSO) new intelligent algorithm, which mainly proposed for continuous problems. It has advantages ...

Journal: :Computers & OR 2006
Kenneth Sörensen Marc Sevaux

A newmetaheuristic for (combinatorial) optimization is presented: memetic algorithms with population management or MA|PM. An MA|PM is a memetic algorithm, that combines local search and crossover operators, but its main distinguishing feature is the use of distance measures for population management. Population management strategies can be developed to dynamically control the diversity of a sma...

2001
A. MITTERER K. KNÖDLER

We study the benefits of Genetic Algorithms, in particular the crossover operator, in constructing experimental designs that are D-optimal. To this purpose, we use standard Monte Carlo algorithms such as DETMAX and k-exchange as the mutation operator in a Genetic Algorithm. Compared to the heuristics, our algorithms are slower but yield better results. Key-Words: Genetic Algorithm, Memetic Algo...

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