Many significant engineering and scientific problems involve optimization of some criteria over a combinatorial configuration space. The two methods most often used to solve these problems effectively—simulated annealing (SA) and genetic algorithms (GA)—do not easily lend themselves to massive parallel implementations. Simulated annealing is a naturally serial algorithm, while GA involves a sel...

With a goal to optimize the element positions to reduce the peak sidelobe level (PSLL) of the array pattern, a modified real Genetic Algorithms (MGA) for the synthesis of sparse linear arrays is described. The multiple optimization constrains include the number of elements, the aperture and the minimum element spacing. The advanced new approach reduces the size of the searching area of GA by me...

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the appl...

Significant research has been carried out recently to find the optimal path in network routing. Among them, the evolutionary algorithm approach is an area where work is carried out extensively. We in this paper have used particle swarm optimization (PSO) and genetic algorithm (GA) for finding the optimal path and the concept of region based network is introduced along with the use of indirect e...

This paper describes an optimal approach for feature subset selection to classify the leaves based on Genetic Algorithm (GA) and Kernel Based Principle Component Analysis (KPCA). Due to high complexity in the selection of the optimal features, the classification has become a critical task to analyse the leaf image data. Initially the shape, texture and colour features are extracted from the lea...

genetic algorithm (ga) has been extensively used in recent decades to solve many optimization problems in various fields of science and engineering. in most cases, the number of iterations is the only criterion which is used to stop the ga. in practice, this criterion will lead to prolong execution times to ensure proper solution. a novel approach is presented in this article as the approximate ...

We present experimental results of applying various nature-inspired optimization techniques to real-world problems from the areas of diagnosis, configuration, planning, and pathfinding. The optimization techniques we investigate include the traditional Genetic Algorithm (GA), discrete (binary and integer-based) Particle Swarm Optimization (DPSO), relatively new Extremal Optimization (EO), and r...

In this study, an improved bit-array representation method for structural topology optimization using the Genetic Algorithm (GA) is proposed. The issue of representation degeneracy is fully addressed and the importance of structural connectivity in a design is further emphasized. To evaluate the constrained objective function, Deb’s constraint handling approach is further developed to ensure th...

In this paper based on the conception of evolution, the Genetic Algorithm (GA) is applied to parameters optimization of proportional-integral-derivative (PID) controller for continuous stirred tank reactor (CSTR).The mathematical model of experimental system had been approximated near the operating point for the GA algorithm to adjust PID parameter for the minimum integral of time multiplied by...