Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

Ali, Zulfiqar; Shahzad, Waseem

doi:10.14569/ijacsa.2017.080108

Cited by 11 publications

(3 citation statements)

References 18 publications

(23 reference statements)

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“…Ali and Shahzad provided a comparative analysis of several AntMiner versions. However, their experiments used the default parameter setting [80]. is is imperative to optimize the parameters for all of the variations before conducting such experiments for a fair comparison.…”

Section: Future Research Directionsmentioning

confidence: 99%

Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

Hossain

Ema

Sohn

2022

Applied Computational Intelligence and Soft Computing

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The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique.

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Section: Future Research Directionsmentioning

confidence: 99%

Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

Hossain

Ema

Sohn

2022

Applied Computational Intelligence and Soft Computing

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“…Ant colony optimization (ACO) [6] is a very powerful optimization heuristic for combinatorial optimization prob- lems, Vehicle Routing algorithms [7] [8], and NP-complete problems. Ant Colony Optimization (ACO) is inspired by ants behavior to find the shortest paths between their colony and the source of food.…”

Section: Introductionmentioning

confidence: 99%

The Proposal of a Distributed Algorithm for Solving the Multiple Constraints Parking Problem

Hassoune¹,

Dachry²,

Moutaouakkil³

et al. 2018

ijacsa

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The parking problem in big cities has become one of the key causes of the city traffic congestion, driver frustration and air pollution.So to avoid these problems, parking monitoring is an important solution. Recently many new technologies have been developed that allows vehicle drivers to effectively find the free parking places in the city but these systems still limited because they don 't take into consideration road networks constraints. In this paper, We design a distributed system that will help drivers to find the optimal route between their positions and an indoor parking in the city taking into consideration a set of constraints such as (distance, traffic, amount of fuel in the car, available places in te parking, and parking cost). We propose a distributed technique based on multi objective Ant Colony Optimisation (ACO). The proposed method aim to manage multi objective parking problem in real time using the behavior of real ants and multi agent systems to decrease the traffic flow and to find the optimal route for drivers.

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“…The main objective is to propose a distributed environment based on multi-agent entities to create a parallel system to solve the garbage collection problem using the ACS (Ant colony System) algorithm [23] to compute the best route for every vehicle and control the different entities that collaborate together for constructing and monitoring the process of waste collection. The system is based on three main layers, each one is controlled by an intelligent agents which are characterized by a specific behavior, we use agents to collect real time information about the state of the vehicle capacity, other agents have been created to travel between the set of bins and find the optimal routes which minimize the total distance covered by each vehicle and a controller agent is defined to control and manage the traffic of communication between the set of agents and supervising the state of vehicles when some trucks arrive at their capacity limit this agent will be able to create alternatives routes for the rest of trucks with the collaboration of the others agents.…”

Section: Introductionmentioning

confidence: 99%

Distributed Swarm Optimization Modeling for Waste Collection Vehicle Routing Problem

Elgarej¹,

Mansouri²,

Youssfi³

et al. 2017

ijacsa

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Abstract-In this paper, we consider a complex garbage collection problem, where the residents of a particular area dispose of recyclable garbage, which is collected and managed using a fleet of trucks with different weight capacities and volume. This tour is characterized by a set of constraints such as the maximum tour duration (in term of distance and the timing) consumed to collect wastes from several locations. This problem is modeled as a garbage collection vehicle routing problem, which aims to minimize the cost of traveling routes (minimizing the distance traveled) by finding optimal routes for vehicles such that all waste bins are emptied and the waste is driven towards the disposal locations. We propose a distributed technique based on the Ant Colony system Algorithm to find optimal routes that help vehicles to visit all the wastes bins using interactive agents consumed based on the behavior of real ants. The designed solution will try to create a set of layers to control and manage the waste collection, each layer will be handled by an intelligent agent which is characterized by a specific behavior, in this architecture a set of behaviors have been designed to optimizing routes and control the real time capacity of vehicles. Finally, manage the traffic messages between the different agents to select the best solutions that will be assigned to each vehicle. The developed solution performs well compared to the traditional solution on small cases.

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Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

Cited by 11 publications

References 18 publications

Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

The Proposal of a Distributed Algorithm for Solving the Multiple Constraints Parking Problem

Distributed Swarm Optimization Modeling for Waste Collection Vehicle Routing Problem

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