FEC 2011 Abstracts


Full Papers
Paper Nr: 4
Title:

SIMULATED ANNEALING METHOD WITH DIFFERENT NEIGHBORHOODS FOR SOLVING THE CELL FORMATION PROBLEM

Authors:

Luong Thuan Thanh, Jacques A. Ferland, Nguyen Dinh Thuc and Van Hien Nguyen

Abstract: In this paper we solve the cell formation problem with different variants of the simulated annealing method obtained by using different neighborhoods of the current solution. The solution generated at each iteration is obtained by using a diversification of the current solution combined with an intensification to improve this solution. Different diversification and intensification strategies are combined to generate different neighborhoods. The most efficient variant allows improving the best-known solution of one of the 35 benchmark problems commonly used by authors to compare their methods, and reaching the best-known solution of 30 others.

Paper Nr: 5
Title:

EVOLUTIONARY STRATEGIES FOR THE ACADEMIC CURRICULUM BALANCED PROBLEM

Authors:

Lorna V. Rosas-Tellez, Jose L. Martínez-Flores and Vittorio Zanella-Palacios

Abstract: The Balanced Academic Curriculum Problem (BACP) is considered an optimization problem, which consist in the assignment of courses in periods that form an academic curriculum so that the prerequisites are satisfied and the courses load is balanced for students. The BACP is a constraint satisfaction problem classified as NP- Hard. In this paper we present the solution to a modified problem BACP where the loads can be the equals or different for each one of the periods and is allowed to have some courses in a specific period. This problem is modeled as an integer programming problem, for which had been obtained solutions for some of their instances with HyperLingo but not for all. Therefore, we propose the use of evolutionary strategies for its solution. The results obtained for the instances of the modified and the original BACP, proposed in the CSPLib, showing that with the use of evolutionary strategies is possible to find the solution for instances of the problem that with the formal method is not possible to find.

Paper Nr: 6
Title:

A HYBRID PSO ALGORITHM FOR THE CVRP PROBLEM

Authors:

Yucheng Kao and Mei Chen

Abstract: The Capacitated Vehicle Routing Problem (CVRP) has been studied over five decades. The goal of CVRP is to minimize the total distance travelled by vehicles under the constraints of vehicles’ capacity. Because CVRP is a kind of NP-hard problem, a number of meta-heuristics have been proposed to solve the problem. The objective of this paper is to propose a hybrid algorithm combining Combinatorial Particle Swarm Optimization (CPSO) with Simulated Annealing (SA) for solving CVRP. The experimental results show that the proposed algorithm can be viewed as an effective approach for solving the CVRP.

Short Papers
Paper Nr: 2
Title:

THREE-DIMENSIONAL POINT-CLOUD REGISTRATION USING A GENETIC ALGORITHM AND THE ITERATIVE CLOSEST POINT ALGORITHM

Authors:

D. Torres and F. J. Cuevas

Abstract: We present a method for three-dimensional surface registration which utilizes a Genetic Algorithm (GA) to perform a coarse alignment of two scattered point clouds followed by a slight variation of the Iterative Closest Point (ICP) algorithm for a final fine-tuning. In this work, in order to improve the time of convergence, a sampling method consisting of three steps is used: 1) sample over the geometry of the clouds based on a gradient function to remove easily interpolating singularities; 2) a random sampling of the clouds and 3) a final sampling based on the overlapping areas between the clouds. The presented method requires no more than 25% of overlapping surface between the two scattered point clouds and no rotational or translational information is needed. The proposed algorithm has shown a good convergence ratio with few generations and usability through automated applications such as object digitalization and reverse engineering.

Paper Nr: 3
Title:

AN INVESTIGATION INTO THE USE OF SWARM INTELLIGENCE FOR AN EVOLUTIONARY ALGORITHM OPTIMISATION - The Optimisation Performance of Differential Evolution Algorithm Coupled with Stochastic Diffusion Search

Authors:

Mohammad Majid al-Rifaie, John Mark Bishop and Tim Blackwell

Abstract: The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be one of the future approaches in the Evolutionary Computation (EC). This work narrates the early research on using Stochastic Diffusion Search (SDS) – a swarm intelligence algorithm – to empower the Differential Evolution (DE) – an evolutionary algorithm – over a set of optimisation problems. The results reported herein suggest that the powerful resource allocation mechanism deployed in SDS has the potential to improve the optimisation capability of the classical evolutionary algorithm used in this experiment. Different performance measures and statistical analyses were utilised to monitor the behaviour of the final coupled algorithm.

Posters
Paper Nr: 8
Title:

ARTIFICIAL NEURAL NETWORKS FOR INTEGRATION OF HVAC COMPONENTS IN BUILDING AUTOMATION - Optimizing Energy-efficiency in Residential Buildings Regarding Space Heating and Automated IAQ-control

Authors:

Tobias Teich and Danny Szendrei

Abstract: Improving building standards and facility services in residential buildings is one major effort for future energy savings. Due to current facility standards and tightened legal restrictions, automated air ventilation (AVS) can contribute large potentials towards energy consumption downsizing. Yet another savings potential can be achieved by providing homogenous media allocation in central heating systems. (Szendrei, 2010) One major effort for energetic optimisation is seen in the integration of AVS and space heating systems in building automation frameworks. As heating losses by window airing refer to faulty user behavior, the parameters: room temperature Jr and indoor air quality (IAQ), expressed by CO2-concentration and relative humidity, are possible subjects for building automation. Building-automation bus systems ensure a holistic energy management and control while maintaining thermal comfort at high level. Since the interferences between space heating, air ventilation and building physics are highly complex, integrative support- and management systems are required. In this paper the effort of transferring heat from intermediate high temperature level zones (IHTL), such as bathrooms and kitchens into long term medium temperature level zones (LMTL) by using air ventilation systems with heat recovery is presented (D. Szendrei and Worms, 2011). Furthermore the implications of hydraulic homogenous mass flows regarding heat energy savings are named as examples for support- and management system design. After naming all relevant energetic parameters, the design of Artificial Neural Networks (ANN) with respect to the presented energetic application is presented. In section 3 we present all relevant input data, required for energetic optimisation, and the basic neural algorithm. As an example, the automatic adjustment of set temperatures for space heating in residential buildings is described.