Differential evolution with an evolution path: a DEEP evolutionary algorithm

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs

SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing

Simulations of the Hyades

Context: Using the recent observational data of R\"oser et al. we present $N$-body simulations of the Hyades open cluster. Aims: We make an attempt to determine initial conditions of the Hyades cluster at the time of its formation in order to reproduce the present-day cumulative mass profile, stellar mass and luminosity function (LF). Methods: We performed direct $N$-body simulations of the Hyades in an analytic Milky Way potential that account for stellar evolution and include primordial binaries in a few models. Furthermore, we applied a Kroupa (2001) IMF and used extensive ensemble-averaging. Results: We find that evolved single-star King initial models with King parameters $W_0 = 6-9$ and initial particle numbers $N_0 = 3000$ provide good fits to the observational present-day cumulative mass profile within the Jacobi radius. The best-fit King model has an initial mass of $1721\ M_\odot$ and an average mass loss rate of $-2.2 \ M_\odot/\mathrm{Myr}$. The K-band LFs of models and observations show a reasonable agreement. Mass segregation is detected in both observations and models. If 33% primordial binaries are included the initial particle number is reduced by 5% as compared to the model without primordial binaries. Conclusions: The present-day properties of the Hyades can be well reproduced by a standard King or Plummer initial model when choosing appropriate initial conditions. The degeneracy of good-fitting models can be quite high due to the large dimension of the parameter space. More simulations with different Roche-lobe filling factors and primordial binary fractions are required to explore this degeneracy in more detail.Comment: 14 pages, 16+1 figures, hopefully final version, contains a note added in proo