09h00 - 09h25
Diving Heuristics Revisited
Diving heuristics are a general purpose approach to find integer-feasible solutions for mixed integer linear programs. The aim is an exploration of a root-leaf path of the Branch-and-Bound‐tree. We present a new approach for the backtracking procedure and a new diving criterion.
09h25 - 09h50
Solving Complex Optimization Problems with the CAT Metaheuristic
We present CAT (for Collaborating Agent Teams), an agent-based metaheuristic. Using this
approach, the model is divided into several sub-models; each being solved using specific
algorithms. Solutions to sub-models are then integrated into solutions of the complete model.
The method’s components are presented and implementation guidelines are provided.
09h50 - 10h15
A New Hybrid Genetic Algorithm and Particle Swarm Optimization
In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The performance of the two algorithms has been evaluated using several experiments.
10h15 - 10h40
Variable Neighborhood Search Metaheuristic for the MaxMinSum (p-dispersion-sum) Problem
Dispersion problems impose a challenge on heuristic solution procedures. Among
different variations of the dispersion models, MaxMinSum problem has not been well
explored in the literature. In this work we have developed several heuristics based on the
Variable Neighborhood Search metaheuristic framework, including various greedy
constructive procedures and different shaking strategies to solve this problem.