Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

Ordinary differential equations of the first order linear differential equations complex numbers and linear algebra simultaneous linear differential equations numerical methods the descriptive theory of nonlinear differential equations mechanical systems and electric circuits Fourier series Fourier integrals and Fourier transforms the Laplace transformation partial differential equations Bessel functions and Legendre polynomials applications and further properties of matrices vector analysis the calculus of variations analytic functions of a complex variable infinite series in the complex plane the theory of residues conformal mapping.

Advanced Engineering Mathematics. By C. R. Wylie. Pp. xiv, 813. 1966. (McGraw-Hill.)

A new initial population strategy has been developed
to improve the genetic algorithm for solving the well-known combinatorial
optimization problem, traveling salesman problem. Based on the k-means
algorithm, we propose a strategy to restructure the traveling route by reconnecting
each cluster. The clusters, which randomly disconnect a link to connect
its neighbors, have been ranked in advance according to the distance among
cluster centers, so that the initial population can be composed of the random
traveling routes. This process is -means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different
TSP examples selected from TSPLIB have been carried out. The results show
that KIP can decrease best error value of random initial population strategy
and greedy initial population strategy with the ratio of approximately between
29.15% and 37.87%, average error value between 25.16% and 34.39% in the
same running time.

/pdf/an-improved-genetic-algorithm-with-initial-population-31ozagxvyl.pdf

An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

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Facility Layout And Location An Analytical Approach

Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types

Three dimensional bin packing problems arise in industrial applications like container ship loading, pallet loading, plane cargo management and warehouse management, etc. In this paper, a hybrid genetic algorithm (GA) is used for regular 3D strip packing. The Genetic Algorithm is hybridized with the presented Deepest Bottom Left with Fill (DBLF) method. Several heuristic methods have also been used for comparison with the hybrid GA.

A hybrid genetic algorithm for packing in 3d with deepest bottom left with fill method

A variable iterated greedy algorithm for the traveling salesman problem with time windows

A hybrid iterated greedy algorithm for total tardiness minimization in permutation flowshops

Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information, such as climate and previous load demand data. In this paper, a genetic programming approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the southeast of Turkey. The empirical results demonstrate successful load forecast with a low error rate.

https://www.mdpi.com/2297-8747/13/2/71/pdf

Korhan Karabulut

Papers

A hybrid genetic algorithm for packing in 3d with deepest bottom left with fill method

A variable iterated greedy algorithm for the traveling salesman problem with time windows

A hybrid iterated greedy algorithm for total tardiness minimization in permutation flowshops

Long Term Energy Consumption Forecasting Using Genetic Programming

Modeling and optimization of multiple traveling salesmen problems: An evolution strategy approach