Titlebook: Handbook of Heuristics; Rafael Martí,Panos M.‘Pardalos,Mauricio G. C. Rese Reference work 2018 Springer International Publishing AG, part

B-cell

Multi-objective Optimizationtaheuristics that have been used for multi-objective optimization. Although special emphasis is made on evolutionary algorithms, other metaheuristics, such as particle swarm optimization, artificial immune systems, and ant colony optimization, are also briefly discussed. Other topics such as applica

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cultivated

過(guò)分

Theory of Local Searche often may not expect to find an algorithm that is guaranteed to return an optimal solution in a reasonable amount of time, i.e., in polynomial time. Therefore, one often resorts to heuristic methods that return good, suboptimal solutions in reasonable running times. Local search is a heuristic met

Maximizer

Variable Neighborhood Descent is based on the simple fact that different neighborhood structures do not usually have the same local minimum. Thus, the local optima trap problem may be resolved by deterministic change of neighborhoods. VND may be seen as a local search routine and therefore could be used within other metaheurist

inspiration

Ant Colony Optimization: A Component-Wise Overviewese algorithms are nowadays collectively known as the ant colony optimization (ACO) metaheuristic. This chapter gives an overview of the history of ACO, explains in detail its algorithmic components, and summarizes its key characteristics. In addition, the chapter introduces a software framework tha

Chandelier

Evolutionary Algorithms broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic engineering concerns; however, all EAs essentially operate by maintaining a population of potential solutions and in some way artificially ‘evolving’ that population over time. Particularly well-known categ

altruism

Genetic Algorithmside variety of fields. The first part of this chapter is devoted to the revision of the basic components for the design of GAs. We illustrate this construction process through its application for solving three widely known optimization problems as knapsack problem, traveling salesman problem, and re

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GRASProblems. Each GRASP iteration is usually made up of a construction phase, where a feasible solution is constructed, and a local search phase which starts at the constructed solution and applies iterative improvement until a locally optimal solution is found. Typically, the construction phase of GRAS

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ajor unsolved problems in orthopedic surgery. Current surgical options include extensive arthrodesis, massive bone grafting sometimes with bone transport or amputations associated with impaired mobility and function, high costs, high risks of failure, and high complication rates. In addition, young