Biased random-key genetic algorithm for fiber installation in Optical Network Optimization

The Container Pre-marshalling Problem (CPMP) has the significant effect of reducing ship berthing time, and can help in increasing terminal turnover rate. In order to solve the CPMP, this research proposes a Multi-objectives Memetic Discrete Differential Evolution algorithm (MODDE). To date, existing research in CPMP only focuses on single-objective approaches. However, this is not a suitable approach due to the considerable effort required to validate the hard constraints of CPMP. Therefore, this work aims at addressing the effect of minimizing the number of miss-overlaid containers on the total number of movements in building the final feasible bay layout by embedding it in the multi-objectives evaluation function. The proposed algorithm combines the Discrete Differential Evolution mutation with the Memetic Algorithm evolutionary steps in order to find high quality CPMP solutions, achieve high convergence rate and avoid premature convergence and local optima problems. In addition, it improves the exploration and exploitation capabilities of the algorithm. The standard pre-marshalling benchmark dataset (i.e., Bortfeldt-Forster) is used to evaluate the effectiveness of the proposed algorithm. The experimental results reveal that the proposed MODDE algorithm can find good solutions on instances of the standard pre-marshalling benchmarks. This demonstrates that using the multi-objectives approach with a combination of the Discrete Differential Evolution mutation and the Memetic Algorithm evolutionary is a suitable approach for solving multi-objectives CPMP.

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“…Containers of different dimensions are stored in different stacks (Goulart, De Souza, Dias, & Noronha, 2011).…”

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confidence: 99%

Multi-Objectives Memetic Discrete Differential Evolution Algorithm for Solving the Container Pre-Marshalling Problem

Mustafa

Ayob

Nazri

et al. 2018

Journal of Information and Communication Technology

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“…For example, BRKGA was applied to solve the fiber installation in an optical network optimization problem [8]. The objective function was to minimize the cost of the optical components necessary to operate the network.…”

mentioning

confidence: 99%

Biased Random Key Genetic Algorithm for Multi-user Earth Observation Scheduling

Tangpattanakul¹,

Jozefowiez²,

López³

2014

Recent Advances in Computational Optimization

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This paper presents a biased random key genetic algorithm, or BRKGA, for solving a multi-user observation scheduling problem. BRKGA is an efficient method in the area of combinatorial optimization. It is usually applied to single objective problem. It needs to be adapted for multi-objective optimization. This paper considers two adaptations. The first one presents how to select the elite set, i.e., good solutions in the population. We borrow the elite selection methods from efficient multi-objective evolutionary algorithms. For the second adaptation, since the multi-objective optimization needs a set of solutions on the Pareto front, we investigate the idea to obtain several solutions from a single chromosome. Experiments are conducted on realistic instances, which concern the multi-user observation scheduling of an agile Earth observing satellite.

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A Biased Random Key Genetic Algorithm with Local Search Chains for Molecular Docking

Leonhart

Dorn

2019

Applications of Evolutionary Computation

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Biased random-key genetic algorithm for fiber installation in Optical Network Optimization

Cited by 9 publications

References 24 publications

Multi-Objectives Memetic Discrete Differential Evolution Algorithm for Solving the Container Pre-Marshalling Problem

Multi-Objectives Memetic Discrete Differential Evolution Algorithm for Solving the Container Pre-Marshalling Problem

Biased Random Key Genetic Algorithm for Multi-user Earth Observation Scheduling

A Biased Random Key Genetic Algorithm with Local Search Chains for Molecular Docking

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