HEURISTICS FOR MULTIPLE KNAPSACK PROBLEM

ABSTRACT The Multiple Knapsack problem (MKP) is a hard combinatorial optimization problem with large application, which embraces many practical problems from different domains, like cargo loading, cutting stock, bin-packing, financial and other management, etc. It also arise as a subproblem in several more complex problems like vehicle routing problem and the algorithms to solve these problems will benefit from any improvement in the field of MKP. The aim of this paper is to compare different kind of heuristic models, statics and dynamics. The heuristics are used by an Ant Colony Optimization (ACO) algorithm to construct solutions to the MKP

Analyse inter-critère basée sur les fonctions de croyance pour l'analyse GPS

International audienceIn this paper we present an application of a new Belief Function-based Inter-Criteria Analysis (BF-ICrA) approach for Global Positioning System (GPS) Surveying Problems (GSP). GPS surveying is an NP-hard problem. For designing Global Positioning System surveying network, a given set of earth points must be observed consecutively. The survey cost is the sum of the distances to go from one point to another one. This kind of problems is hard to be solved with traditional numerical methods. In this paper we use BF-ICrA to analyze an Ant Colony Optimization (ACO) algorithm developed to provide near-optimal solutions for Global Positioning System surveying problem

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Simulated Annealing for Grid Scheduling Problem

Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data storage or network resources across dynamic and geographically dispersed organizations. The goal of grid tasks scheduling is to achieve high system throughput and to match the application need with the available computing resources. This is matching of resources in a non-deterministically shared heterogeneous environment. The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. To obtain good methods to solve this problem a new area of research is implemented. This area is based on developed heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper we introduce a tasks scheduling algorithm for grid computing. The algorithm is based on simulated annealing method. The paper shows how to search for the best tasks scheduling for grid computing

8th Workshop on Computational Optimization

This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2015. It presents recent advances in computational optimization. The volume includes important real life problems like parameter settings for controlling processes in bioreactor, control of ethanol production, minimal convex hill with application in routing algorithms, graph coloring, flow design in photonic data transport system, predicting indoor temperature, crisis control center monitoring, fuel consumption of helicopters, portfolio selection, GPS surveying and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization problems.

Near-native Protein Structure Simulation

The protein folding problem is a fundamental problem in computational molecular biology and biochemical physics. The high resolution 3D structure of a protein is the key to the understanding and manipulating of its biochemical and cellular functions. All information necessary to fold a protein to its native structure is contained in its amino-acid sequence. Proteins structure could be calculated from knowledge of its sequence and our understanding of the sequence-structure relationships. Various optimization methods have been applied to formulation of the folding problem. There are two main approaches. The one is based on properties of homologous proteins. Other is based on reduced models of proteins structure like hydrophobic-polar (HP) protein model. After that, the folding problem is defined like optimization problem. It is a hard optimization problem and most of the authors apply Monte Carlo or metaheuristic methods to solve it. In this paper other approach will be used. By HP model is explained the structures of proteins conformation observed by biologists and is studied the correspondence between the primary and tertiary structures of the proteins