A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> a: MUTATION OPERATION <s> Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants. <s> BIB001 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> a: MUTATION OPERATION <s> Abstract Today, VLSI technology has taken a fundamental role in developing most of the high-tech electronic circuits. Even though VLSI design is renowned for its smaller size, lower cost, lower power, high reliability and high functionality, the design process takes long time and produces high risk. So, to obtain the knowledge regarding the different contributions on VLSI design, about 52 papers on the design of VLSI using optimization are reviewed here. Accordingly, VLSI design optimization is analyzed through different bio-inspired algorithms and the performance measures of different VLSI experimentations are compared. Further, various improvements on Self-Adaptive Particle Swarm Optimization (SA-PSO) and VLSI design optimization, without the adoption of bio-inspired algorithms, are examined. Additionally, the floor planning problem of VLSI is considered and reviewed. Eventually, this paper provides the diverse research gaps and challenges in VLSI design, which may be helpful for the authors and the philosophers to contribute for further research. <s> BIB002
After initializing the population, DE employs the mutation operation to produce a mutant vector. Reference BIB001 gives five commonly used variation strategies as follows: where V t i = {v t i,1 , v t i,2 , . . . , v t i,D } is mutant vector. The indices r 1 , r 2 , r 3 , r 4 , r 5 are mutually exclusive integers randomly generated within the range BIB002 NP] , and these indices are randomly generated once for each mutant vector. The scaling factor F is a positive control parameter for scaling the difference vector. X t best is the best individual vector with the best fitness value in the population at generation t.