The bi-objective set packing problem is a multi-objective combinatorial optimization problem similar to the well-known set covering/partitioning problems. To our knowledge, this problem has surprisingly not yet been studied. In order to resolve a practical problems encountered in railway infrastructure capacity planning, procedures for computing a solution to this bi-objective combinatorial pro...

Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the e ort that these algorithms are allowed to make and also the strategy and search behavior [1]. Adjusting these parameters allows to increase the algorithms' performances with respect to differ...

Metaheuristics are probabilistic optimization algorithms which are applicable to a wide range of optimization problems. Bio-inspired, also called nature-inspired, optimization algorithms are the most widely-known metaheuristics. The general scheme of bio-inspired algorithms consists in an initial stage of randomly generated solutions which evolve through search operations, for several generatio...

Experience has shown that a crafted combination of concepts of different metaheuristics can result in robust combinatorial optimization schemes and produce higher solution quality than the individual metaheuristics themselves, especially when solving difficult real-world combinatorial optimization problems. This chapter gives an overview of different ways to hybridize GRASP (Greedy Randomized A...

The No Free Lunch (NFL) theorems for search and optimization are reviewed and their implications for the design of metaheuristics are discussed. The theorems state that any two search or optimization algorithms are equivalent when their performance is averaged across all possible problems and even over subsets of problems fulfilling certain constraints. The NFL results show that if there is no ...

the network design problem (ndp) is one of the important problems in combinatorial optimization. among the network design problems, the multicommodity capacitated network design (mcnd) problem has numerous applications in transportation, logistics, telecommunication, and production systems. the mcnd problems with splittable flow variables are np-hard, which means they require exponential time t...

In this paper, we consider the fuzzy fixed-charge transportation problem (FFCTP). Both of fixed and transportation cost are fuzzy numbers. Contrary to previous works, Electromagnetism-like Algorithms (EM) is firstly proposed in this research area to solve the problem. Three types of EM; original EM, revised EM, and hybrid EM are firstly employed for the given problem. The latter is being firstl...

We provide the global optimization community with new optimality proofs for 6 deceptive benchmark functions (5 bound-constrained functions and one nonlinearly constrained problem). These highly multimodal nonlinear test problems are among the most challenging benchmark functions for global optimization solvers; some have not been solved even with approximate methods. The global optima that we r...