A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> C. DE 1) INSPIRATION AND BASIC IDEAS OF DE <s> A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation. <s> BIB001 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> C. DE 1) INSPIRATION AND BASIC IDEAS OF DE <s> Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE. <s> BIB002
The DE was proposed by Storn and Price on the basis of evolutionary ideas such as Genetic Algorithm (GA) in 1997 BIB001 . The essence is a multi-objective (continuous variables) evolutionary algorithm. And it is used for solving the overall optimal solution in multidimensional space. In community of DE, the individual trial solutions which constitute a population are called parameter vectors or genomes BIB002 . DE generates a population of individuals by encoding with a floating vector, and the optimization process includes mutation, hybridization, and selection operations. The basic idea is described as follows: Starting from a randomly generated initial population, a new individual is generated by summing the vector difference of any two individuals in the population with a third individual, and the fitness value is used to determine whether to retain the old individual. Through continuous evolution, retaining good individuals, and eliminating inferior individuals, DE guides search to the optimal solution.