Heuristic algorithms and learning techniques: applications to the graph coloring problem

Porumbel, Daniel Cosmin

doi:10.1007/s10288-011-0193-5

Cited by 6 publications

(2 citation statements)

References 131 publications

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“…Hill-climbing [2] usually finds the local optimum instead of the global warm. These algorithms are hard to solve exactly classical NP-complete problems such as Hamiltonian cycle problem [3], traveling salesman problem [4], and coloring problem [5], etc. This is also the reason that metaheuristic algorithms (MAs) enter the blowout period.…”

Section: Introductionmentioning

confidence: 99%

An enhanced chimp optimization algorithm for continuous optimization domains

Jia

Sun

Zhang

et al. 2021

Complex Intell. Syst.

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Chimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman’s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.

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Section: Introductionmentioning

confidence: 99%

An enhanced chimp optimization algorithm for continuous optimization domains

Jia

Sun

Zhang

et al. 2021

Complex Intell. Syst.

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“…The problem is NP-hard problem and thus exact methods are difficult to solve for more than 20 -50 OWTs. Heuristic algorithms generally can be used to solve NP-hard problem [19] [20]. There are several algorithms, such as Dijkstra's Algorithm and Genetic Algorithm (GA), could be possible to solve the NP-hard problem.…”

Section: Introductionmentioning

confidence: 99%

Multi-ACO Application in Routing and Scheduling Optimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms

Zhang¹

2018

JPEE

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Reducing the operation and maintenance (O & M) cost is one of the potential actions that could reduce the cost of energy produced by offshore wind farms. This article attempts to reduce O & M cost by improving the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet. Scheduling and routing of maintenance fleet is a non-linear optimization problem with high complexity and a number of constraints. A heuristic algorithm, Ant Colony Optimization (ACO), was modified as Multi-ACO to be used to find the optimal scheduling and routing of maintenance fleet. The numerical studies showed that the proposed methodology was effective and robust enough to find the optimal solution even if the number of offshore wind turbine increases. The suggested approaches are helpful to avoid a time-consuming process of manually planning the scheduling and routing with a presumably suboptimal outcome.

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