The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto–optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. ...

This paper presents a differential evolution algorithm to solve the permutaion flowshop sequencing problem with makespan criterion. Differential evolution is one of the latest evolutionary optmization algorithm applied to continuous optimization problems where members of population use chromosomes based on floating-point numbers to represent candidate solutions. In this paper we also present a ...

Heuristic evolutionary optimization algorithms are the solutions to many engineering optimization problems. Differential evolution (DE) is a real stochastic evolutionary parameter optimization in current use.DE does not require more control parameters compared to other evolutionary algorithms. Master Production Scheduling (MPS) is posed as one of multi objective parameter optimization problems ...

This work presents an analysis of the static Aging operator for different evolutionary algorithms: two immunological algorithms (OptIA and Clonalg), a standard genetic algorithm SGA, and Differential Evolution (DE) algorithm. The algorithms were tested against standard benchmarks in both unconstrained and dynamic optimisation problems. This work analyses whether the aging operator improves the ...

The population-based evolutionary algorithms have emerged as powerful mechanism for finding optimum solutions of complex optimization problems. A promising new evolutionary algorithm, differential evolution, has garnered significant attention in the engineering optimization research. Differential evolution has the advantage of incorporating a relatively simple and efficient form of mutation and...

In real life we are often confronted with dynamic optimization problems whose optima change over time. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In this paper, we propose an evolutionary model that combines the differential evolution algorithm with cellular automata to address dynamic optimization problems. In the pro...

Economic Load Dispatch (ELD) is an important and difficult optimization problem in power system planning. This article aims at addressing two practically important issues related to ELD optimization: (1) analyzing the ELD problem from the perspective of evolutionary optimization; (2) developing effective algorithms for ELD problems of large scale. The first issue is addressed by investigating t...

To explore the effect of spatial locality, crowding differential evolution is incorporated with spatial locality for multimodal optimization. Instead of random trial vector generations, it takes advantages of spatial locality to generate fitter trial vectors. Experiments were conducted to compare the proposed algorithm (CrowdingDE-L) with the state-of-the-art algorithms. Further experiments wer...

finding an optimal path for a robot in a soccer field involves different parameters such as the positions of the robot, positions of the obstacles, etc. due to simplicity and smoothness of ferguson spline, it has been employed for path planning between arbitrary points on the field in many research teams. in order to optimize the parameters of ferguson spline some evolutionary or intelligent al...