A Hybrid Metaheuristic of Integrating Estimation of Distribution Algorithm with Tabu Search for the Max-Mean Dispersion Problem

This paper presents a hybrid metaheuristic that combines estimation of distribution algorithm with tabu search (EDA-TS) for solving the max-mean dispersion problem. The proposed EDA-TS algorithm essentially alternates between an EDA procedure for search diversification and a tabu search procedure for search intensification. The designed EDA procedure maintains an elite set of high quality solutions, based on which a conditional preference probability model is built for generating new diversified solutions. The tabu search procedure uses a fast 1-flip move operator for solution improvement. Experimental results on benchmark instances with variables ranging from 500 to 5000 disclose that our EDA-TS algorithm competes favorably with state-of-the-art algorithms in the literature. Additional analysis on the parameter sensitivity and the merit of the EDA procedure as well as the search balance between intensification and diversification sheds light on the effectiveness of the algorithm.

Evolutionary algorithm based on discrete ITO process for travelling salesman problems

Since dozens years ago, various metaheuristic methods, such as genetic algorithm, ant colony algorithms, have been successfully applied to combinational optimization problem. However, as one of the members, ITO algorithm has only been employed in continuous optimization, it needs further design for combinational optimization problem. In this paper, a discrete ITO algorithm inspired by ITO stochastic process is proposed for travelling salesman problems (TSPs). Some key operators, such as move operator, wave operator, are redesigned to adapt to combinational optimization. Moreover, the performance of ITO algorithm in different parameter selections and the maintenance of population diversity information are also studied. By combining local search methods (such as 2-opt and LK-opt) with ITO algorithm, our computational results of the TSP problems show that ITO algorithm is currently one of the best-performing algorithms for these problems.

An Autonomous Artificial Designer of Thermal Energy Systems: Part 2—Solution Algorithm

A knowledge-based approach to automated conceptual design (flowsheet synthesis) of thermal energy systems with strong interactions between heat/power/chemical transformations is presented. In Part 1, formulation of a thermal design problem is stated in terms of input/output specification, component interaction, feasibility constraints, and penalty function. The problem is then decomposed in inner problems that deal with a set of elementary processes, and outer problems that find a network of components approximating the optimum set of elementary processes. A design state is evaluated using a special form of fundamental equation for steady-state open thermodynamic systems based on a “temperature interval” concept. In Part 2 of this paper, an algorithm is presented. The algorithm makes use of the state evaluation function, transformation operators, and the plausible move operator to search through a space of the design states. A simple closed-cycle gas turbine is employed to illustrate the behavior of the “artificial designer” as it advances from a certain given design to more sophisticated schemes.