Local search algorithms operate by making small changes to candidate solutions with the aim of reaching new and improved solutions. The problem is that often the search will become trapped at sub optimal states from where there are no improving neighbours. Much research has gone into creating schemes to avoid these local optima and various strategies exist mainly based around altering the accep...

This paper analyzes the detection of stagnation states in iterated local search algorithms. This is done considering elements such as the population size, the length of the encoding and the number of observed non-improving iterations. This analysis isolates the features of the target problem within one parameter for which three different estimations are given: two static a priori estimations an...

We compare six metaheuristic optimization algorithms applied to solving the travelling salesman problem. We focus on three classical approaches: genetic algorithms, simulated annealing and tabu search, and compare them with three recently developed ones: quantum annealing, particle swarm optimization and harmony search. On top of that we compare all results with those obtained with a greedy 2-o...

This paper develops Order Acceptance for an Integrated Production-Distribution Problem in which Batch Delivery is implemented. The aim of this problem is to coordinate: (1) rejecting some of the orders (2) production scheduling of the accepted orders and (3) batch delivery to maximize Total Net Profit. A Mixed Integer Programming is proposed for the problem. In addition, a hybrid meta-heuristic...

For efficiency reasons, neighbourhoods in local search algorithms are often shrunk by only considering moves modifying variables that actually contribute to the overall penalty. These are known as conflicting variables. This is a well-known technique for speeding up search. State-of-the-art solutions to, e.g., the progressive party problem exploit this with great success. We propose a way of au...

Local search algorithms, such as simulated annealing, tabu search, and local hill climbers attempt to optimize a solution to a problem by making locally improving modifications to a candidate solution. They rely on a neighborhood operator to restrict the search to a typically small set of possible successor states. The genetic algorithm mutation operator, likewise, enables the exploration of th...

The Walksat local search algorithm has previously been extended to handle quantification over variables. This greatly reduces model sizes, but in order to guide greedy moves the algorithm still maintains a set of violated clauses. For very large problems, or at the start of a search, this can cause memory problems. We design a new local search algorithm that does not maintain this set and is th...

Many optimization algorithms require gradients of the model functions, but computing accurate gradients can be computationally expensive. We study the implications of using inexact gradients in the context of the multilevel optimization algorithm MG/Opt. MG/Opt recursively uses (typically cheaper) coarse models to obtain search directions for finer-level models. However, MG/Opt requires the gra...

in the science of operation research and decision theory, selection is the most important process. selection is a process that studies multiple qualitative and quantitative criteria, related to the science of management, which are mostly incompatible with each other. the multi criteria selection of a renewable energy portfolio is one of the main issues considered in multi criteria literature. i...