Cosolver2B: An Efficient Local Search Heuristic for the Travelling Thief Problem

Real-world problems are very difficult to optimize. However, many researchers have been solving benchmark problems that have been extensively investigated for the last decades even if they have very few direct applications. The Traveling Thief Problem (TTP) is a NP-hard optimization problem that aims to provide a more realistic model. TTP targets particularly routing problem under packing/loading constraints which can be found in supply chain management and transportation. In this paper, TTP is presented and formulated mathematically. A combined local search algorithm is proposed and compared with Random Local Search (RLS) and Evolutionary Algorithm (EA). The obtained results are quite promising since new better solutions were found.Comment: 12th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA) 2015. November 17-20, 201

An Evolutionary approach to microstructure optimisation of stereolithographic models.

Abstract- The aim of this work is to utilize an evolutationary algorithm to evolve the microstructure of an object created by a stereolithography machine. This should be optimised to be able to withstand loads applied to it while at the same time minimizing its overall weight. A two part algorithm is proposed which evolves the topology of the structure with a genetic algorithm, while calculating the details of the shape with a separate, deterministic, iterative process derived from standard principles of structural engineering. The division of the method into two separate processes allows both flexibility to changed design parameters without the need for re-evolution, and scalability of the microstructure to manufacture objects of increasing size. The results show that a structure was evolved that was both light and stable. The overall shape of the evolved lattice resembled a honeycomb structure that also satisfied the restrictions imposed by the stereolithography machine.

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods