DynDE : a differential evolution for dynamic optimization problems

This paper presents an approach of using Differential Evolution (DE) to solve dynamic optimization problems. Careful setting of parameters is necessary for DE algorithms to successfully solve optimization problems. This paper describes DynDE, a multi-population DE algorithm developed specifically to solve dynamic optimization problems that doesn't need any parameter control strategy for the F or CR parameters. Experimental evidence has been gathered to show that this new algorithm is capable of efficiently solving the moving peaks benchmark

Advanced planning in vertically integrated wine supply chains

Alternative citation: Evolutionary computation for dynamic optimization problems, 2013 / Shengxiang Yang, Xin Yao (eds.), Ch. 17 pp. 433-463This chapter gives detailed insights into a project for transitioning a wine manufacturing company from a mostly spreadsheet driven business with isolated silo-operated planning units into one that makes use of integrated and optimised decision making by use of modern heuristics. We present a piece of the puzzle - the modelling of business entities and their silo operations and optimizations, and pave the path for a further holistic integration to obtain company-wide globally optimised decisions. We argue that the use of “Computational Intelligence” methods is essential to cater for dynamic, time-variant and non-linear constraints and solve today’s real-world problems exemplified by the given wine supply chain.Maksud Ibrahimov, Arvind Mohais, Maris Ozols, Sven Schellenberg and Zbigniew Michalewiczhttp://link.springer.com/chapter/10.1007%2F978-3-642-38416-5_1