Tabu - local search mechanism for Mega Process genetic algorithm

2005 ◽  
Author(s):  
Y. Hanada ◽  
T. Hiroyasu ◽  
M. Miki
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Search Mechanism

scholarly journals A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Leilei Cao ◽  
Lihong Xu ◽  
Erik D. Goodman
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Its Region ◽  
Bat Algorithm ◽  
Greedy Strategy ◽  
Search Mechanism ◽  
Genotype Space

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

scholarly journals Local Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers

2021 ◽  
Vol 11 (3) ◽  
pp. 7283-7289
Author(s):  
F. A. Alshammari ◽  
G. A. Alshammari ◽  
T. Guesmi ◽  
A. A. Alzamil ◽  
B. M. Alshammari ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Local Search ◽  
Operating Conditions ◽  
Power System Stabilizers ◽  
Local Optima ◽  
Electromechanical Oscillations ◽  
Search Mechanism ◽  
Wide Range ◽  
Metaheuristic Techniques

This study presents a metaheuristic method for the optimum design of multimachine Power System Stabilizers (PSSs). In the proposed method, referred to as Local Search-based Non-dominated Sorting Genetic Algorithm (LSNSGA), a local search mechanism is incorporated at the end of the second version of the non-dominated sorting genetic algorithm in order to improve its convergence rate and avoid the convergence to local optima. The parameters of PSSs are tuned using LSNSGA over a wide range of operating conditions, in order to provide the best damping of critical electromechanical oscillations. Eigenvalue-based objective functions are employed in the PSS design process. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation proved that the proposed controller provided competitive results compared to other metaheuristic techniques.

scholarly journals Integrating Iterative Crossover Capability in Orthogonal Neighborhoods for Scheduling Resource-Constrained Projects

2013 ◽  
Vol 21 (2) ◽  
pp. 341-360 ◽  
Author(s):  
Reza Zamani
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Project Scheduling ◽  
Search Space ◽  
Search Method ◽  
Evolutionary Search ◽  
Resource Constrained ◽  
Search Mechanism ◽  
Benchmark Instances ◽  
Project Scheduling Problem

An effective hybrid evolutionary search method is presented which integrates a genetic algorithm with a local search. Whereas its genetic algorithm improves the solutions obtained by its local search, its local search component utilizes a synergy between two neighborhood schemes in diversifying the pool used by the genetic algorithm. Through the integration of these two searches, the crossover operators further enhance the solutions that are initially local optimal for both neighborhood schemes; and the employed local search provides fresh solutions for the pool whenever needed. The joint endeavor of its local search mechanism and its genetic algorithm component has made the method both robust and effective. The local search component examines unvisited regions of search space and consequently diversifies the search; and the genetic algorithm component recombines essential pieces of information existing in several high-quality solutions and intensifies the search. It is through striking such a balance between diversification and intensification that the method exploits the structure of search space and produces superb solutions. The method has been implemented as a procedure for the resource-constrained project scheduling problem. The computational experiments on 2,040 benchmark instances indicate that the procedure is very effective.

scholarly journals Benchmarking RCGAu on the Noiseless BBOB Testbed

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Babatunde A. Sawyerr ◽  
Aderemi O. Adewumi ◽  
M. Montaz Ali
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Search Space ◽  
Test Functions ◽  
Multimodal Functions ◽  
Search Mechanism ◽  
Random Direction ◽  
Real Coded Genetic Algorithm ◽  
Search Capability

RCGAu is a hybrid real-coded genetic algorithm with “uniform random direction” search mechanism. Theuniform random directionsearch mechanism enhances the local search capability of RCGA. In this paper, RCGAu was tested on the BBOB-2013 noiseless testbed using restarts till a maximum number of function evaluations (#FEs) of 105×Dare reached, whereDis the dimension of the function search space. RCGAu was able to solve several test functions in the low search dimensions of 2 and 3 to the desired accuracy of 108. Although RCGAu found it difficult in getting a solution with the desired accuracy 108for high conditioning and multimodal functions within the specified maximum #FEs, it was able to solve most of the test functions with dimensions up to 40 with lower precisions.

A hybrid genetic algorithm with local search

2001 ◽  
Vol 59 (1-2) ◽  
pp. 107-120 ◽  
Author(s):  
G Vivó-Truyols ◽  
J.R Torres-Lapasió ◽  
A Garrido-Frenich ◽  
M.C Garcı́a-Alvarez-Coque
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm

scholarly journals Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika

2018 ◽  
Vol 8 (1) ◽  
pp. 99
Author(s):  
A. Y. Erwin Dodu ◽  
Deny Wiria Nugraha ◽  
Subkhan Dinda Putra
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Public Hospital ◽  
Memetic Algorithm ◽  
Optimal Schedule ◽  
Scheduling System ◽  
Local Search Methods ◽  
Evolution Algorithm ◽  
Central Sulawesi

The problem of midwife scheduling is one of the most frequent problems in hospitals. Midwife should be available 24 hours a day for a full week to meet the needs of the patient. Therefore, good or bad midwife scheduling result will have an impact on the quality of care on the patient and the health of the midwife on duty. The midwife scheduling process requires a lot of time, effort and good cooperation between some parties to solve this problem that is often faced by the Regional Public Hospital Undata Palu Central Sulawesi Province. This research aimed to apply Memetics algorithm to make scheduling system of midwifery staff at Regional Public Hospital Undata Palu Central Sulawesi Province that can facilitate the process of midwifery scheduling as well as to produce optimal schedule. The scheduling system created will follow the rules and policies applicable in the hospital and will also pay attention to the midwife's preferences on how to schedule them according to their habits and needs. Memetics algorithm is an optimization algorithm that combines Evolution Algorithm  and Local Search method. Evolution Algorithm in Memetics Algorithm generally refers to Genetic Algorithm so that the characteristics of Memetics Algotihm are identical with  Genetic Algorithm characteristics with the addition of Local Search methods. Local Search in Memetic Algorithm aims to improve the quality of an individual so it is expected to accelerate the time to get a solution.

A hybrid genetic algorithm for multiobjective problems with activity analysis-based local search

2009 ◽  
Vol 193 (1) ◽  
pp. 195-203 ◽  
Author(s):  
Gerald Whittaker ◽  
Remegio Confesor ◽  
Stephen M. Griffith ◽  
Rolf Färe ◽  
Shawna Grosskopf ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm ◽  
Activity Analysis ◽  
Multiobjective Problems
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