The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristi...

Optimization problems are fundamental to mathematics and computer science. Symmetric optimization problems are interesting and difficult, appearing in several contexts such as the search for combinatorial objects. However, the standard techniques to solve optimization problems often fail in the presence of symmetry. This work examines symmetric optimization problems and applies new techniques t...

The Dial-a-ride problem with time windows (DARPTW) assigns and schedules users transport requests to a fleet of vehicles enabled to fulfill the required service. The literature offers different heuristics for solving DARPTW problems, which are based on the extension of traditional graph search algorithms. On the other hand, the approach through Genetic Algorithms (GA) has been experienced in pr...

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 bene...

The permutation flow shop scheduling problem (PFSP) is one of the most well known and well studied production scheduling problems with strong industrial background. Most scheduling problems are combinatorial optimization problems which are too difficult to be solved optimally, and hence heuristics are used to obtain good solutions in a reasonable time. The specific goal of this paper is to inve...