(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 2, 2016 Reflected Adaptive Differential Evolution with Two External Archives for Large-Scale Global Optimization Rashida Adeeb Khanum Jinnah College for Women University of Peshawar Khyber Pakhtunkhwa, Pakistan. Email: adeeb maths@yahoo.com Nasser Tairan College of Computer Science, King Khalid University Abha, Saudi Arabia Email: nmtairan@kku.edu.sa Muhammad Asif Jan Department of Mathematics Kohat University of Science & Technology Khyber Pakhtunkhwa, Pakistan Email: majan@kust.edu.pk Wali Khan Mashwani Department of Mathematics Kohat University of Science & Technology Khyber Pakhtunkhwa, Pakistan Email: mashwanigr8@gmail.com Abdel Salhi Department of Mathematical Sciences University of Essex Colchester, CO4 3SQ, U.K. Email: as@essex.ac.uk Abstract—JADE is an adaptive scheme of nature inspired algorithm, Differential Evolution (DE). It performed considerably improved on a set of well-studied benchmark test problems. In this paper, we evaluate the performance of new JADE with two external archives to deal with unconstrained continuous large-scale global optimization problems labeled as Reflected Adaptive Differential Evolution with Two External Archives (RJADE/TA). The only archive of JADE stores failed solutions. In contrast, the proposed second archive stores superior solutions at regular intervals of the optimization process to avoid premature convergence towards local optima. The superior solutions which are sent to the archive are reflected by new potential solutions. At the end of the search process, the best solution is selected from the second archive and the current population. The performance of RJADE/TA algorithm is then extensively evaluated on two test beds. At first on 28 latest benchmark functions constructed for the 2013 Congress on Evolutionary Computation special session. Secondly on ten benchmark problems from CEC2010 Special Session and Competition on Large-Scale Global Optimization. Experimental results demonstrated a very competitive perfor- mance of the algorithm. Keywords—Adaptive differential evolution; large scale global optimization; archives. I. I NTRODUCTION Optimization deals with finding the optimal solution for single or multi-objective functions [1]. An unconstrained single objective optimization problem can be stated as follows: Minimize f (x), (1) where f (x) denotes the objective function, and x = (x 1 ,x 2 , ..., x n ) T is an n-dimensional real vector. DE [2] is a most popular bio-inspired scheme for finding the global optimum x * of problem (1). The heuristic is essentially an evolutionary one and relies on the usual genetic operators of mutation and crossover. DE is easy to understand and implement, has a few parameters to control, and is robust. There is no doubt that DE is a remarkable optimizer for many optimization problems. However, it has few drawbacks like, stagnation, premature convergence, and loss of diversity. Since it is a global optimizer, so its local search ability is not that good. More details can be found in [3]. To enhance the performance of DE, many modifications to the classic DE have been suggested and various variants of DE are proposed. A novel work is done by Wang et al. [4], in which they utilized orthogonal crossover instead of binomial and exponential crossover. A group of researchers have introduced new variants like opposition based DE [5], centroid dependent initialization ciJADE [6], cluster-based population initialization (CBPI) [7] jDE [8], genDE [9], In- dividual dependent Mechanism (IDE) [10] etc. Control pa- rameters adaptation and self-adaptation have devised in [11], [12], jDErpo [13] SaDE [14], JADE [15], [16], EPSDE [17], IDE [18], SHADE [19]L-SHADE [20] [21], EWMA-DECrF [22]. Cooperative coevolution have been brought into DE for large scale optimization [23]. Some researchers applied it to problems from the discrete domain [24], [25], while others are taking the advantage of its global searching in the continuous domains [4], [26]–[28]. In another experiment, adaptive variant of DE, the so- called JADE [15], is proposed for numerical optimization. It has shown performance improvement over the state-of-the- art algorithms, jDE [8], SaDE [29] and DE/rand/1/bin [2] according to the reported results in [15] and [30]. However, JADE is not reliable; on some problems. For instance, it finds the global optima in some runs, but it can also be trapped in local optima [30]. To improve the reliability of JADE, in this paper, we introduce two new strategies in JADE and thus propose Reflected Adaptive Differential Evolution with Two External Archives (RJADE/TA). The rest of this paper is organized as follows. Section II de- scribes the basic DE and JADE algorithm. Section III presents www.ijacsa.thesai.org 675 | Page