The Graph Partitioning Problem (GPP) is one of the most studied NPcomplete problems notable for its broad spectrum of applicability such as in VLSI design, data mining, image segmentation, etc. Due to its high computational complexity, a large number of approximate approaches have been reported in the literature. Hybrid algorithms that are based on adaptations of popular metaheuristic technique...

A metaheuristic algorithms provide effective methods to solve complex problems using f inite sequence of instructions. It can be defined as an iterative search process that eff iciently performs the exploration and exploitation in the solution space aiming to eff icient ly f ind near optimal solutions. This iterative process has adopted various natural intelligences and aspirations. In this wor...

Branch and Bound Algorithms based on Interval Arithmetic permit to solve exactly continuous (as well as mixed) non-linear and non-convex global optimization problems. However, their intrinsic exponential time-complexities do not make it possible to solve some quite large problems. The idea proposed in this paper is to limit the memory available during the computations of such a global optimizat...

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...

The multidimensional knapsack problem is defined as an optimization problem that is NP-hard combinatorial. The multidimensional knapsack problems have large applications, which include many applicable problems from different area, like cargo loading, cutting stock, bin-packing, financial and other management, etc. This paper reviews some researches published in the literature. The concentrate i...

This paper discusses the problem of estimating, on the basis of a given number of say N experiments, the expected performance of a metaheuristic on a class I of benchmark problem instances. The problem of the empirical estimation of the expected behavior of a stochastic optimization algorithm has great relevance both in academic studies and in practical applications. This is particularly true f...

Local search metaheuristic algorithms are proven & powerful combinatorial optimization strategies to tackle hard problems like traveling salesman problem. These algorithms explore & evaluate neighbors of a single solution. Time Consuming LSM algorithms can be improved by parallelizing exploration & evaluation of neighbors of a solution. GPU architecture is suitable for algorithms of single prog...

Path-relinking is major enhancement to heuristic search methods for solving combinatorial optimization problems, leading to significant improvements in both solution quality and running times. We review its fundamentals and implementation strategies, as well as advanced hybridizations with more elaborate metaheuristic schemes such as genetic algorithms and scatter search. Numerical examples are...

Two general-purpose metaheuristic algorithms for solving multiobjective stochastic combinatorial optimization problems are introduced: SP-ACO (based on the Ant Colony Optimization paradigm) which combines the previously developed algorithms S-ACO and P-ACO, and SPSA, which extends Pareto Simulated Annealing to the stochastic case. Both approaches are tested on random instances of a TSP with tim...