Converging Marriage in Honey-Bees Optimization and Application to Stochastic Dynamic Programming

In this paper, we first refine a recently proposed metaheuristic called “Marriage in Honey-Bees Optimization” (MBO) for solving combinatorial optimization problems with some modifications to formally show that MBO converges to the global optimum value. We then adapt MBO into an algorithm called “Honey-Bees Policy Iteration” (HBPI) for solving infinite horizon-discounted cost stochastic dynamic programming problems and show that HBPI also converges to the optimal value.     

Application of a heuristic search technique for the improvement of spray zones cooling conditions in continuously cast steel billets

The process of casting occupies an important place in the metallurgical industry, and the entire world of the metal user. In the past, the ingot casting–rolling (slabbing, blooming, or billeting) process was commonly used. The continuous casting process has largely replaced this earlier method because of the inherent advantages of energy savings, enhanced productivity, higher yield and reduced costs. However, continuous casting process is not without of its problems. Considerable effort has been made by many researches to establish adequate design, operation and maintenance of continuous casting machine to ensure metallurgical quality of the final product. One of the most severe defects in continuous casting products is concerned with the cracks provoked by improper design of the spray cooling system. The aim of this work is to develop a two dimensional heat transfer model based on the finite difference method in order to calculate the strand temperatures and the solid shell profile along the machine. An Artificial Intelligence heuristic search procedure interacts with the numerical model to determine the improved cooling conditions for the sprays zones of a real continuous caster for the production of quality billets.     

计算智能与力学反问题中的若干问题

计算智能是生物智能的计算模拟,包括人工神经网络、模糊系统和进化计算三个主要部分．参考国内外有关计算智能与力学反问题研究中的某些问题,并结合作者的研究工作,介绍了有关课题的研究状况以及目前的研究进展．     

Branch and win: OR tree search algorithms for solving combinatorial optimisation problems

Currently, most combinatorial optimisation problems have to be solved, if the optimum solution is sought, using general techniques to explore the space of feasible solutions and, more specifically, through exploratory enumerative procedures in trees and search graphs. We propose Branch and Win, a general formulation for understanding and synthesising the different tree search procedures that have been presented in the literature of operations research as well as in that of artificial intelligence. Several general ideas are also presented, whose application allows designing new hybrid search algorithms, in order to implement the procedure.