scholarly journals Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

2021 ◽  
Author(s):  
Qinghua Gu ◽  
Qian Wang ◽  
Neal N. Xiong ◽  
Song Jiang ◽  
Lu Chen
Keyword(s):  
Evolutionary Algorithm ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Multi Objective ◽  
Random Forest Models ◽  
Probability Operator ◽  
Expensive Optimization ◽  
Optimization Efficiency ◽  
Discrete Optimization Problems

AbstractSurrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.

A Continuous Strategy to Solve a Class of Discrete Optimization Problems

2010 ◽  
Vol 36 ◽  
pp. 279-286 ◽  
Author(s):  
Roberto Quirino do Nascimento ◽  
Edson Figueiredo Lima ◽  
Rubia Mara de Oliveira Santos
Keyword(s):  
Discrete Optimization ◽  
Optimization Problems ◽  
Discrete Optimization Problems

scholarly journals Подход к решению задачи оптимизации логистики агрохолдинга

2013 ◽  
pp. 139-144
Author(s):  
Е. В. Скакалина
Keyword(s):  
Information Technology ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Implementation Process ◽  
Optimal Solutions ◽  
New Class ◽  
Modern Computer ◽  
Foreign Countries ◽  
Intensive Development ◽  
Discrete Optimization Problems

У роботі наведено короткий аналіз використання інформаційних технологій в аграрному напрямі. Вказу-ється на можливість удосконалення управління проце-сом реалізації логістики великих агрохолдингів за раху-нок використання ефективного методу побудови оп-тимальних рішень для узагальнень задачі про призна-чення. Представлений новий клас дискретних оптимі-заційних задач. Звертається увага на інтенсивний роз-виток логістики у зарубіжних країнах на основі викори-стання сучасних комп'ютерних технологій. The paper presents a brief analysis of the use of information technology in the agricultural area. The possibility of improvement of management of logistics implementation process of large agricultural holdings through the use of an effective method of optimal solutions constructing for generalizations of the assignment problem is shown. A new class of discrete optimization problems is presented. The attention is drawn to the intensive development of logistics in foreign countries on the basis of use of modern computer technologies.

scholarly journals Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

2021 ◽  
Vol 6 (4 (114)) ◽  
pp. 6-14
Author(s):  
Maan Afathi
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Fuzzy Logic Control ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Hybrid Evolutionary Algorithm ◽  
Logic Control

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

Sign in / Sign up

Export Citation Format

Share Document