Many real life problems contain imprecise variables, constraints and objectives. Fuzzy set theory gives an opportunity to handle imprecise terms in such situations. Two-sided assembly line balancing (2sALB) problem which is a generalization of the well known simple assembly line balancing problem can also be modeled more realistically by employing fuzzy approaches. Such an approach is presented...

A hyper-heuristic is an automated methodology for selecting or generating heuristics to solve hard computational search problems. The main feature distinguishing these methods is that they explore a search space of heuristics (rather than a search space of potential solutions to a problem). The goal is that hyper-heuristics will lead to more general systems that are able to automatically operat...

We compare in this paper the best heuristic methods known up to now to solve the flow shop sequencing problem and we improve the complexity of the best one. Next, we apply to this problem taboo search, a new technique to solve combinatorial optimization problems, and we report computational experiments. Finally a parallel taboo search algorithm is presented and experimental results show that th...

We propose methods to solve time-varying, sensor and actuator (SaA) selection problems for uncertain cyberphysical systems. We show that many SaA selection problems for optimizing a variety of control and estimation metrics can be posed as semidefinite optimization problems with mixed-integer bilinear matrix inequalities (MIBMIs). Although this class of optimization problems is computationally ...

graph theory based methods are powerful means for representing structural systems so that their geometry and topology can be understood clearly. the combination of graph theory based methods and some metaheuristics can offer effective solutions for complex engineering optimization problems. this paper presents a charged system search (css) algorithm for the free shape optimizations of thin-wall...

Combining ideas from evolutionary algorithms, decomposition approaches and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: MoMad. It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MoMad evolves three populations: popula...

Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category problems. Those have feature discrete design domain turn them into set NP-hard Determining the right algorithm for such precious point tends to impact overall cost process. Furthermore,...

The article describes the proposition and implementation of a demonstration, learning and decision support system for the resolution of combinatorial optimization problems under multiple objectives. The system brings together two key aspects of higher education: research and teaching. It allows the user to define modern metaheuristics and test their resolution behavior on machine scheduling pro...

Meta-Heuristics (MH) are the most used optimization techniques to approach Complex Combinatorial Problems (COPs). Their ability to move beyond the local optimums make them an especially attractive choice to solve complex computational problems, such as most scheduling problems. However, the knowledge of what Meta-Heuristics perform better in certain problems is based on experiments. Classic MH,...