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.
Highlights
Optimization deals with finding the optimal solution for single or multi-objective functions [1]
Experimental validations for the proposed RJADE/TA are conducted on a set of 28 new and complex test functions [39] provided by CEC 2013 special session and a 1000 dimensional functions designed for CEC 2010 competition on large scale global optimization problems [40]
In the CEC 2013 test suite, the previously proposed composition functions of CEC 2005 [2] are enhanced and additional test functions are considered for real parameter single objective optimization
Summary
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 = (x1, x2, ..., xn)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). There is no doubt that DE is a remarkable optimizer for many optimization problems. 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]
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