The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

In this paper, we present a new method, called the genetic simulated annealing ant colony
system with particle swarm optimization techniques, for solving the traveling salesman …

The genetic algorithm (GA) is a population based meta-heuristic global optimization
technique for dealing with complex problems with very large search space. The population …

Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary
computation that plays a significant role in obtaining meaningful solutions to complex …

Genetic Algorithm (GA), a random universal evolutionary search technique that imitates the
principle of biological evolution has been applied in solving various problems in different …

This study deals with a mixed static and dynamic optimization of four‐parameter functionally
graded material (FGM) doubly curved shells and panels. The two constituent functionally …

The genetic algorithm is a popular meta-heuristic optimization technique whose
performance depends on the quality of the initial population and the crossover operator …

The multiple traveling salesman problem (MTSP) involves scheduling m> 1 salesmen to visit
a set of n> m nodes so that each node is visited exactly once. The objective is to minimize …

Evidently, information security is a sine qua non in the “modern life, With its sick hurry, its
divided aims, Its heads o'ertaxed, its palsied hearts,...”. To protect our data against …

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization
technique for dealing with complex problems with a very large search space. The population …