Most of the real world optimization problems are multi-objective in nature. Recently, Evolutionary algorithms are gaining popularity for solving Multi-Objective Optimization Problems (MOOPs) due to their inherent advantages over traditional methods. In this paper, Differential Evolution (an evolutionary algorithm that is significantly faster and robust for optimization problems over continuous ...

This paper investigates a hybrid evolutionary-based design system for automated sizing of analog integrated circuits (ICs). A new algorithm, called competitive co-evolutionary differential evolution (CODE), is proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through el...

The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CP...

This paper aims at optimizing the parameters involved in stress analysis of perforated plates, in order to achieve the least amount of stress around the square-shaped holes located in a finite isotropic plate using metaheuristic optimization algorithms. Metaheuristics may be classified into three main classes: evolutionary, physics-based, and swarm intelligence algorithms. This research uses Ge...

the multiple traveling salesman problem (mtsp) is a generalization of the famous traveling salesman problem (tsp), where more than one salesman is used in the solution. although the mtsp is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. this paper presents an efficient and evolutionary optimization algorith...

In the last years the evolutionary computation domain is developed more and more rapidly. Differential Evolution is one of the representatives of this domain. It is a recently invented evolutionary algorithm, which nowadays is gaining more and more popularity. Originally proposed for continuous unconstraint optimization, it have been enlarged both and for mixed optimization and for handling non...

 Evolutionary multi-objective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multi-objective problem. The purpose of this paper is to describe a newly developed evolutionary approach --Paretobased multi-objective differential evolution (MODE). In this paper, the concept of differential evolution, which is well-known in the continuou...

A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

A non-linear, multi-objective optimization method is presented that seeks to maximize free product recovery of light non-aqueous phase liquids (LNAPLs) while minimizing operation cost. This approach combines FEHM (Finite Element Heat and Mass transfer code), a groundwater model that can simulate LNAPL transport in the subsurface, with two evolutionary algorithms: the genetic algorithm (GA) and ...