Mazhar Ansari Ardeh

Mazhar Ansari Ardeh
Victoria University of Wellington · Department of Electronics and Computer Science

MSc

About

16
Publications
729
Reads
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115
Citations
Citations since 2017
14 Research Items
115 Citations
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Publications

Publications (16)
Article
Full-text available
The uncertain capacitated arc routing problem is an NP-hard combinatorial optimisation problem with a wide range of applications in logistics domains. Genetic programming hyper-heuristic has been successfully applied to evolve routing policies to effectively handle the uncertain environment in this problem. The real world usually encounters differe...
Article
The uncertain capacitated arc routing problem has many real-world applications in logistics domains. Genetic programming is a promising approach to training routing policies to make real-time decisions and handle uncertain events effectively. In the real world, there are various problem domains and no single routing policy can work effectively in a...
Chapter
Genetic Programming Hyper-heuristics (GPHHs) have been successfully applied in various problem domains for automatically designing heuristics such as dispatching rules in scheduling and routing policies in vehicle routing. In the real world, it is normal to encounter related problem domains, such as the vehicle routing problem with different object...
Chapter
The Uncertain Capacited Arc Routing Problem (UCARP) is an important variant of arc routing problems that is capable of modelling uncertainties of real-world scenarios. Genetic Programming is utilised to evolve routing policies for vehicles to enable them to make real-time decisions and handle environment uncertainties. However, when the properties...
Chapter
Full-text available
Uncertain Capacitated Arc Routing Problem (UCARP) is a challenging optimization problem. Genetic Programming (GP) has been successfully applied to train routing policies (heuristics to make decisions in real time rather than a fixed solution) to respond to uncertain environments effectively. However, the effectiveness of routing policy is scenario...
Conference Paper
Full-text available
The Uncertain Capacitated Arc Routing Problem (UCARP) is an important combinatorial optimisation problem. Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP. However, when the scenario changes, the current routing policy can no longer work effectively, and one has...
Method
Full-text available
Meta-heuristic algorithms are of considerable importance in solving optimization problems. This importance is more highlighted when the problems to be optimized are too complicated to achieve a solution using conventional methods or, the traditional methods are somehow not applicable for solving them. Imperial Competitive Algorithm has been proved...
Article
Full-text available
Meta-heuristic algorithms are of considerable importance in solving optimization problems. This importance is more highlighted when the problems to be optimized are too complicated to achieve a solution using conventional methods or, the traditional methods are somehow not applicable for solving them. Imperial Competitive Algorithm has been proved...
Article
In this research, the Game of Elimination is introduced as a novel multiple-period competitive game in a shared market. In this game, each player has a single goal of gaining more profit from the shared market and, in order to achieve this goal, tries to eliminate other players from the market by attracting their customers. In other words, the goal...

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Cited By

Projects

Projects (2)
Archived project
Meta-heuristic algorithms are of considerable importance in solving optimization problems. This importance is more highlighted when the problems to be optimized are too complicated to achieve a solution using conventional methods or, the traditional methods are somehow not applicable for solving them. Imperial Competitive Algorithm has been proved to be an efficient and effective meta-heuristic optimization algorithm and it has been successfully applied in many scientific and engineering problems. By introducing the concept of explorers and retention policy, the original algorithm is enhanced with a dynamic population mechanism in this paper and hence, the performance of the Imperial Competitive Algorithm is improved. Performance of the proposed modification is tested with experiments of optimizing real-values functions and results are compared with results obtained with the original Imperialistic Competitive Algorithm, Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing. Also, the applicability of the proposed improvement is verified by optimizing a ship propeller design problem.
Project
BenchmarkFcns is a personal effort to provide a public and free repository of sources and documents for well-known benchmark optimization functions. The project is open-sourced, hosted on GitHub and is available at www.benchmarkfcns.xyz .