A COMPARATIVE STUDY OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHMS FOR SOLVING BINARY INTEGER LINEAR PROGRAMMING PROBLEMS

Abstract

The main objective of this project is to study and compare the quality solution and convergence speed of the two evolutionary algorithms; genetic algorithms and particle swarm optimization by solving binary integer linear programming problems. The two approaches find a solution to a given objective function employing different procedures and computational techniques; as a result, their performance can be evaluated and compared. In particle swarm optimization, the particles are initialized by a randomized velocity at the beginning of the search process in the swarm are then changed according to sigmoid function and velocity particle have been considered. The change in the particle values is determined by their previous position and the best-known position of the particle over the entire search space have been considered. In genetic algorithms, the chromosomes in population have been mate through process called crossover thus producing new chromosomes named offspring which its genes composition is the combination of their parent and also mutation in their gene. The particle swarm optimization and genetic algorithms are coded in MATLAB R2019a and six binary integer linear programming problems which have different characteristics are optimized. This project is to compare the quality solution and convergence speed of the genetic algorithms and particle swarm optimization by solving binary integer linear programming problems. In terms of quality solution, the experimental results show that the particle swarm optimization is better than genetic algorithms in 2 out of 6 binary integer linear programming problems. But, in terms of convergence speed, the experimental results show that the genetic algorithm is better than particle swarm optimization in 4 out of 6 binary integer linear programming problems.