scholarly journals OptMap: Using Dense Maps for Visualizing Multidimensional Optimization Problems

2021 ◽  
Author(s):  
Mateus Espadoto ◽  
Francisco Rodrigues ◽  
Nina Hirata ◽  
Alexandru Telea
Keyword(s):  
Optimization Problems ◽  
Multidimensional Optimization

SCALABILITY OF BIO-HEURISTICS FOR MULTIDIMENSIONAL OPTIMIZATION PROBLEMS

2021 ◽  
Vol 115 ◽  
pp. 8-14
Author(s):  
Sergey Rodzin ◽  
◽  
Olga Rodzina ◽  
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Search Area ◽  
Multidimensional Optimization

A scalable bio-heuristic algorithm capable of solving multidimensional optimization problems is proposed. Special operators are used to support the diversity of the solution population, to expand the search area for solutions at the expense of less promising solutions. The efficiency of the proposed algorithm is evaluated on a set of multidimensional functions of Grivank, Rastrigin, Rosenbrock, and Schwefel. The indicators of the developed algorithm are com-pared with those of competing algorithms.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Sign in / Sign up

Export Citation Format

Share Document