Two-dimensional placement compaction using an evolutionary approach: a study

The placement problem of two-dimensional objects over planar surfaces optimizing given utility functions is a combinatorial optimization problem. Our main drive is that of surveying genetic algorithms and hybrid metaheuristics in terms of final positioning area compaction of the solution. Furthermore, a new hybrid evolutionary approach, combining a genetic algorithm merged with a non-linear compaction method is introduced and compared with referenced literature heuristics using both randomly generated instances and benchmark problems. A wide variety of experiments is made, and the respective results and discussions are presented. Finally, conclusions are drawn, and future research is defined

Sistemas de apoio à decisão para problemas de localização e roteamento.

O objetivo deste trabalho é mostrar os sistemas de apoio à decisão desenvolvidos para solucionar problemas de localização e roteamento, composto pelos novos enfoques de algoritmos de localização e roteamento e sistemas de informação geográfica Spring, Map Objects, Transcard e Arc View.bitstream/CNPTIA/9959/1/bolpesq7.pdfAcesso em: 28 maio 2008

An integrated quantitative framework for supporting product design : the case of metallic moulds for injection

Tese de Doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Intelligent optimization of Circuit placement on FPGA

Field programmable gate arrays (FPGAs) have revolutionized the way digital systems are designed and built over the past decade. With architectures capable of holding tens of millions of logic gates on the horizon and planned integration of configurable logic into system-on-chip platforms, the versatility of programmable devices expected to increase dramatically. Placement is one of the vital steps in mapping a design into FPGA in order to take best advantage of the resources and flexibility provided by it. Here, we propose to test techniques of Placement Optimization on MCNC Benchmark circuits. PSO (Particle Swarm Optimization) has been implemented on circuit netlist with bounding box as cost function. Alternate cost functions were also employed to verify efficiency of optimization. Furthermore, lazy descent was introduced into the algorithm to impede premature convergence. Different values of acceleration and weighing factors were used in the implementation and corresponding convergence results were analyzed. Keywords- FPGA Placement; Particle Swarm Optimization; MCNC Benchmarks Circuits; Bounding Box driven Placement

The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks

This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical engineering and design challenges. This work proposes new hybrid heuristics consisting of cooperative coevolutionary frameworks and neuro-surrogate optimisation methods for optimising WECs problem in three domains, including position, control parameters, and geometric parameters. Our problem-specific algorithms perform better than existing approaches in terms of higher quality results and the speed of convergence. ii) The second part applies search methods to the optimization of energy output in wind farms. Wind energy has key advantages in terms of technological maturity, cost, and life-cycle greenhouse gas emissions. However, designing an accurate local wind speed and power prediction is challenging. We propose two models for wind speed and power forecasting for two wind farms located in Sweden and the Baltic Sea by a combination of recurrent neural networks and evolutionary search algorithms. The proposed models are superior to other applied machine learning methods. iii) Finally, we investigate the design of water distribution systems (WDS) as another challenging real-world optimisation problem. WDS optimisation is demanding because it has a high-dimensional discrete search space and complex constraints. A hybrid evolutionary algorithm is suggested for minimising the cost of various water distribution networks and for speeding up the convergence rate of search.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202