Self-adaptive simulated binary crossover for real-parameter optimization

Simulated binary crossover (SBX) is a real-parameter recombinationoperator which is commonly used in the evolutionary algorithm (EA) literature. The operatorinvolves a parameter which dictates the spread of offspring solutionsvis-a-vis that of the parent solutions. In all applications of SBX sofar, researchers have kept a fixed value throughout a simulation run. In this paper, we suggest a self-adaptive procedure of updating theparameter so as to allow a smooth navigation over the functionlandscape with iteration. Some basic principles of classicaloptimization literature are utilized for this purpose. The resultingEAs are found to produce remarkable and much better results comparedto the original operator having a fixed value of the parameter. Studieson both single and multiple objective optimization problems are madewith success

Non-elitist Evolutionary Multi-objective Optimizers Revisited

Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs. This paper revisits the performance of non-elitist EMOAs for bi-objective continuous optimization when using an unbounded external archive. This paper examines the performance of EMOAs with two elitist and one non-elitist environmental selections. The performance of EMOAs is evaluated on the bi-objective BBOB problem suite provided by the COCO platform. In contrast to conventional wisdom, results show that non-elitist EMOAs with particular crossover methods perform significantly well on the bi-objective BBOB problems with many decision variables when using the unbounded external archive. This paper also analyzes the properties of the non-elitist selection.Comment: This is an accepted version of a paper published in the proceedings of GECCO 201

Accelerating ant colony optimization by using local search

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.Cataloged from PDF version of thesis report.Includes bibliographical references (page 42-45).Optimization is very important fact in terms of taking decision in mathematics, statistics, computer science and real life problem solving or decision making application. Many different optimization techniques have been developed for solving such functional problem. In order to solving various problem computer Science introduce evolutionary optimization algorithm and their hybrid. In recent years, test functions are using to validate new optimization algorithms and to compare the performance with other existing algorithm. There are many Single Object Optimization algorithm proposed earlier. For example: ACO, PSO, ABC. ACO is a popular optimization technique for solving hard combination mathematical optimization problem. In this paper, we run ACO upon five benchmark function and modified the parameter of ACO in order to perform SBX crossover and polynomial mutation. The proposed algorithm SBXACO is tested upon some benchmark function under both static and dynamic to evaluate performances. We choose wide range of benchmark function and compare results with existing DE and its hybrid DEahcSPX from other literature are also presented here.Nabila TabassumMaruful HaqueB. Computer Science and Engineerin

A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation

Copyright © 2012 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences Vol. 183 Issue 1 (2012), DOI: 10.1016/j.ins.2011.07.013We present a new crossover operator for real-coded genetic algorithms employing a novel methodology to remove the inherent bias of pre-existing crossover operators. This is done by transforming the topology of the hyper-rectangular real space by gluing opposite boundaries and designing a boundary extension method for making the fitness function smooth at the glued boundary. We show the advantages of the proposed crossover by comparing its performance with those of existing ones on test functions that are commonly used in the literature, and a nonlinear regression on a real-world dataset

Мурашиний алгоритм з метафорою агрегації феромонів для глобальної оптимізації

Кваліфікаційна робота включає пояснювальну записку (56 с., 14 рис. 21 табл., 2 додатки). Об’єкт розробки – процес оптимізації дійсної функції багатьох змінних в неперервному просторі. Метою роботи є розробка алгоритму оптимізації мурашиної колонії з метафорою агрегації феромонів для пошуку екстремумів дійсної функції. Запропоновано модифікацію мурашиного алгоритму оптимізації в неперервному просторі у вигляді системи агрегації феромонів з метою покращення точності й сталості результатів. Виконано порівняльний аналіз алгоритму з класичним мурашиним а також з іншими евристичними алгоритмами, які оптимізовані для розв’язку задач в неперервному просторі. Проведена імплементація розробленого алгоритму для деяких відомих тестових функцій. Здійснена на мові програмування C++. Були визначені параметри алгоритму, знайдені оптимальні їх значення. На основі аналізу розробленого алгоритму зроблені висновки, визначені його основні переваги і недоліки.Qualifying work includes explanatory note. The object of development - the process of optimizing the real function of many variables in a continuous space. The aim of the work is to develop an ant colony optimization algorithm with a pheromone aggregation metaphor to find the extremums of a real function. The modification of the ant algorithm optimization in the continuous space with a system of pheromones aggregation is proposed in order to improve the accuracy and stability of the results. The comparative analysis of the algorithm with classical ant as well as other heuristic algorithms optimized for solving problems in a continuous space is performed. Implementation of the developed algorithm for some known test functions has been carried out. Made in the programming language C ++. The parameters of the algorithm were determined and their optimal values were found. Based on the analysis of the developed algorithm, conclusions are drawn, its main advantages and disadvantages are determined