Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

Abstract

This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.

Parameters Identification of Photovoltaic Models Using a Multi-Strategy Success-History-Based Adaptive Differential Evolution

“…This subsection describes an experimental analysis of the effectiveness of three main components which are added to the original algorithm [48]. The three main components include weighted mutation strategy, inferior solution search strategy and Eigen Gaussian random walk strategy.…”

“…In this section, the experiments performed on parameters identification of different PV models including single diode, double diode and PV module are presented to prove the effectiveness of the proposed MLSHADE. In order to make a fair comparison, the benchmark experimental current-voltage data of a solar cell and a solar module are employed, which can be obtained in [48], where a 57 mm diameter commercial RTC France silicon solar cell and a solar module named Photowatt-PWP201 are included [48]. Meanwhile, the lower and upper bounds for each parameter are shown in [48].…”

LSHADE-SPA memetic framework for solving large-scale optimization problems

“…Crossover rate (CR) was adapted by a gradual change of the CR values according to the past experience. In the same year, ANDE as another adaptive DE with novel triangular mutation strategy was proposed [42]. This mutation selects three vectors randomly and sorts them from best to worst.…”

Multiobjective Optimization for a Wireless Ad Hoc Sensor Distribution on Shaped-Bounded Areas

“…Then, the recombination constant and rand are compared in order to check if rand < . Equation (25) briefly shows that if the aforementioned condition is false, the th element of the target vector is selected as the th element of the trial vector. Otherwise the th element of the mutant individual is selected as the th element of the trial vector [23].…”

“…A survey of the applications of DE to different optimization problems shows that DE generally outperforms other evolutionary algorithms [21][22][23]. An updated reference with novel DE algorithm versions and additional applications are found at [24,25]. DE exploits a population of potential solutions to effectively probe the search spaces.…”