Dissimilarity Measures for Population-Based Global Optimization Algorithms

Very hard optimization problems, i.e., problems with a large number of variables and local minima, have been effectively attacked with algorithms which mix local searches with heuristic procedures in order to widely explore the search space. A Population Based Approach based on a Monotonic Basin Hopping optimization algorithm has turned out to be very effective … Read more

Efficiently packing unequal disks in a circle: a computational approach which exploits the continuous and combinatorial structure of the problem

Placing $N$ non-overlapping circles in a smallest container is a well known, widely studied problem that can be easily formulated as a mathematical programming model. Solving this problem is notoriously extremely hard. Recently a public contest has been held for finding putative optimal solutions to a special case in circle packing. The contest saw the … Read more

A Population Based Approach for Hard Global Optimization Problems Based on Dissimilarity Measures

When dealing with extremely hard global optimization problems, i.e. problems with a large number of variables and a huge number of local optima, heuristic procedures are the only possible choice. In this situation, lacking any possibility of guaranteeing global optimality for most problem instances, it is quite difficult to establish rules for discriminating among different … Read more