3 Solution techniques 13 3.1 Combinatorial approach : : : : : : : : : : : : : : : : : : : : : : : : : : 13 3.2 Continuous approach : : : : : : : : : : : : : : : : : : : : : : : : : : : : 14 3.2.1 Examples of embedding : : : : : : : : : : : : : : : : : : : : : : 14 3.2.2 Global approximation : : : : : : : : : : : : : : : : : : : : : : : 15 3.2.3 Continuous trajectories : : : : : : : : : : : : : ...

Difficult nonconvex optimization problems contain a combinatorial number of local optima, making them extremely challenging for modern solvers. We present a novel nonconvex optimization algorithm that explicitly finds and exploits local structure in the objective function in order to decompose it into subproblems, exponentially reducing the size of the search space. Our algorithm’s use of decom...

In this article we present work on chromosome structures for Genetic Algorithms (GAs) based on biological principles. Mainly, the in-uence of non{coding segments on GA behavior and performance is investigated. We compare representations with non{coding sequences at predeened, xed locations with \junk" code induced by the use of Pro-moter/Terminator sequences (ptGA) deening start and end of a co...

In some cases, we may be able to find “efficient” exact algorithms to solve a COP: for example, the problem of finding the shortest paths on a graph, under some reasonable assumptions often met in practise, can be solved by the Dijkstra or Bellman-Ford algorithms, able to provide optimal solutions in running times according to a polynomial function (of small degree). For more complex problems, ...

Examples: Shortest Path, Traveling Salesman, and many many more. . . Just in bioinformatics: Alignments, Threading, Clone-Probe Mapping, Probe Selection, De Novo Peptide Sequencing, SideChain Placement, Maximum-weight Connected Subgraph in PPI Networks, Genome Rearrangements, Cluster Editing, Finding Regulatory Modules, Finding Approximate Gene Clusters, Haplotyping, and many more. . . Example....

the multiple traveling salesman problem (mtsp) is a generalization of the famous traveling salesman problem (tsp), where more than one salesman is used in the solution. although the mtsp is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. this paper presents an efficient and evolutionary optimization algorith...

the set covering problem (scp) is a well-known combinatorial optimization problem. this paper investigates development of a local branching approach for the scp. this solution strategy is exact in nature, though it is designed to improve the heuristic behavior of the mixed integer programming solver. the algorithm parameters are tuned by design of experiments approach. the proposed method is te...

the traveling salesman problem (tsp) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. the tsp is probably the most famous and extensively studied problem in the field of combinatorial optimization. because this problem is an np-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...

In this paper proposes a fuzzy multi-objective hybrid Genetic and Bee colony optimization algorithm(GA-BCO) to find the optimal restoration of loads of power distribution network under fault.Restoration of distribution systems is a complex combinatorial optimization problem that should beefficiently restored in reasonable time. To improve the efficiency of restoration and facilitate theactivity...

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...