In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimization algorithm, the CovarianceMatrix Adaptation Evolution Strategy (CMAES), the Differential Evolution (DE) algorithm and a Particle Swarm Optimization (PSO) algorithm are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization prob...

This paper describes the application of Evolutionary Algorithms (EAs) to the optimisation of a simplified supply chain in an integrated production-inventory-distribution system. The performance of four EAs (Genetic Algorithm (GA), Evolutionary Programming (EP), Evolution Strategies (ES) and Differential Evolution (DE)) was evaluated with numerical sumulations. Results were also compared with ot...

—This paper introduces an improved Differential Evolution algorithm (IDE) which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation alg...

Differential evolution (DE) presents a class of evolutionary computing techniques that appear effective to handle real parameter optimization tasks in many practical applications. However, the performance of DE is not always perfect to ensure fast convergence to the global optimum. It can easily get stagnation resulting in low precision of acquired results or even failure. This paper proposes a...

This paper presents a new hybrid algorithm called QDEPSO (Quantum inspired Differential Evolution with Particle Swarm Optimization) which combines differential evolution (DE), particle swarm optimization method (PSO) and quantum-inspired evolutionary algorithm (QEA) in order to solve the 0-1 optimization problems. In the initialization phase, the QDEPSO uses the concepts of quantum computing as...

In order to improve the optimum speed, crease the diversity of the population and overcome the premature convergence problem in differential evolution(DE) algorithm for solving the complex optimization problems, the chaotic optimization algorithm with powerful local searching capacity and multistrategy are introduced into the DE algorithm in order to propose an improved adaptive differential ev...

This paper presents a modified Differential Evolution (DE) algorithm called OCMODE for solving multi-objective optimization problems. First, the initialization phase is improved by using the opposition based learning. Further, a time varying scale factor F employing chaotic sequence is used which helps to get a well distributed Pareto front by the help of non dominated and crowding distance sor...

Information systems need to be constantly monitored and audited; analysis of security event logs in heavy traffic networks is a challenging task. In this paper we considered Differential Evolution for the intrusion detection problem. We used NSL_KDD dataset for our experiments which is derived from the standard KDD CUP 99 Intrusion Dataset. We also provided the comparative results of the differ...