Super-fit Multicriteria Adaptive Differential Evolution

Abstract

This paper proposes an algorithm to solve the CEC2013 benchmark. The algorithm, namely Super-fit Multicriteria Adaptive Differential Evolution (SMADE), is a Memetic Computing approach based on the hybridization of two algorithmic schemes according to a super-fit memetic logic. More specifically, the Covariance Matrix Adaptive Evolution Strategy (CMAES), run at the beginning of the optimization process, is used to generate a solution with a high quality. This solution is then injected into the population of a modified Differential Evolution, namely Multicriteria Adaptive Differential Evolution (MADE). The improved solution is super-fit as it supposedly exhibits a performance a way higher than the other population individuals. The super-fit individual then leads the search of the MADE scheme towards the optimum. Unimodal or mildly multimodal problems, even when non-separable and ill-conditioned, tend to be solved during the early stages of the optimization by the CMAES. Highly multi-modal optimization problems are efficiently tackled by SMADE since the MADE algorithm (as well as other Differential Evolution schemes) appears to work very well when the search is led by a super-fit individual.

A sinusoidal differential evolution algorithm for numerical optimisation

“…It is clear from the three tables that the proposed SinDE is much more powerful than both other approaches. In fact, it significantly outperforms the calssical DE in 75 cases out of 84 and the LADE in 55 In addition to its comparison to the classical variants, the proposed SinDE is compared against stronger recent variants of the DE algorithm, namely: The Super-fit Multicriteria Adaptive Differential Evolution (SMADE) [30], the Differential Evolution with Concurrent Fitness Based Local Search (DEcfbLS) [31], a powerful variant of the competitive differential Evolution [32] called b6e6rl [33] and the Teaching and Learning Based Self-adaptive DE (TLBSade) [34].…”

A novel optimization algorithm inspired by the creative thinking process

“…Corresponding results of the four comparison algorithms come from Caraffini et al (2013), and they are listed in the three tables too. The best results of the five algorithms are shown in bold.…”

“…Error values and standard deviations smaller than 10 −8 are taken as zero. The experimental results are compared with four state-of-the-art algorithms (SMADE, MDE-pBX, CMAES and CCPSO2) listed in Caraffini et al (2013). In this paper, the parameter settings of COOA are listed in Table 2.…”

Differential Evolution With an Individual-Dependent Mechanism

“…Zhao et al [29] modified 3 EPSDE by incorporating an SaDE type learning scheme. SMADE, proposed in [30], makes use of a multiple mutation strategy consisting of four different mutation operators and selects one operator via a roulette-wheel selection scheme for each target individual in current generation. Researchers in [31] proposed a variant of jDE, i.e., jDEsoo (jDE for single objective optimization), which concurrently applies three different DE strategies in three sub-populations.…”

“…Therefore, the single-mutation operator strategies with composite searching features (such as BDE [18], DEGL [19], JADE [20], ProDE [21], BoRDE [22], and TDE [23]) and the multi-mutation operators strategies with different searching features (such as SaDE [24], [25], TLBSaDE [26], CoDE [27], EPSDE [28], [29], SMADE [30], jDEsoo [31], and SPSRDEMMS [32]) were proposed. Epitropakis et al [18] linearly combined an explorative and an exploitive mutation operator to form a hybrid approach (BDE) with an attempt to balance these two operators.…”