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Article
Publication date: 1 September 2019

Yu Zhou

To plan the urban traffic path using the ant colony algorithm, the composition and functional division of the mobile robot are analyzed. The TSP (Traveling Salesman Problem) is…

Abstract

To plan the urban traffic path using the ant colony algorithm, the composition and functional division of the mobile robot are analyzed. The TSP (Traveling Salesman Problem) is used to deeply understand the traditional ant colony algorithm. Then, based on this, the improvement scheme of the traditional ant colony algorithm is analyzed. The results showed that the artificial potential field method and the A* algorithm improved the performance of the ant colony algorithm. At the initial stage of the search path, the blindness and randomness of the ant colony algorithm due to insufficient pheromone concentration in each path were solved. The local optimal path is avoided with the development of algorithm iteration. Therefore, the improved ant colony algorithm is superior to the traditional ant colony algorithm.

Details

Open House International, vol. 44 no. 3
Type: Research Article
ISSN: 0168-2601

Keywords

Article
Publication date: 26 June 2023

Somia Boubedra, Cherif Tolba, Pietro Manzoni, Djamila Beddiar and Youcef Zennir

With the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding…

Abstract

Purpose

With the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.

Design/methodology/approach

An improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.

Findings

Experimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.

Originality/value

The proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 16 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 4 December 2017

Wu Deng, Meng Sun, Huimin Zhao, Bo Li and Chunxiao Wang

This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one…

Abstract

Purpose

This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one of the most important tasks for airport ground operations, which assigns appropriate airport gates with high efficiency reasonable arrangement.

Design/methodology/approach

In this paper, on the basis of analyzing the characteristics of airport gates and flights, an efficient multi-objective optimization model of airport gate assignment based on the objectives of the most balanced idle time, the shortest walking distances of passengers and the least number of flights at apron is constructed. Then an improved ant colony optimization (ICQACO) algorithm based on the ant colony collaborative strategy and pheromone update strategy is designed to solve the constructed model to fast realize the gate assignment and obtain a rational and effective gate assignment result for all flights in the different period.

Findings

In the designed ICQACO algorithm, the ant colony collaborative strategy is used to avoid the rapid convergence to the local optimal solution, and the pheromone update strategy is used to quickly increase the pheromone amount, eliminate the interference of the poor path and greatly accelerate the convergence speed.

Practical implications

The actual flight data from Guangzhou Baiyun airport of China is selected to verify the feasibility and effectiveness of the constructed multi-objective optimization model and the designed ICQACO algorithm. The experimental results show that the designed ICQACO algorithm can increase the pheromone amount, accelerate the convergence speed and avoid to fall into the local optimal solution. The constructed multi-objective optimization model can effectively improve the comprehensive operation capacity and efficiency. This study is a very meaningful work for airport gate assignment.

Originality/value

An efficient multi-objective optimization model for hub airport gate assignment problem is proposed in this paper. An improved ant colony optimization algorithm based on ant colony collaborative strategy and the pheromone update strategy is deeply studied to speed up the convergence and avoid to fall into the local optimal solution.

Article
Publication date: 15 June 2015

Bundit Manaskasemsak and Arnon Rungsawang

This paper aims to present a machine learning approach for solving the problem of Web spam detection. Based on an adoption of the ant colony optimization (ACO), three algorithms

Abstract

Purpose

This paper aims to present a machine learning approach for solving the problem of Web spam detection. Based on an adoption of the ant colony optimization (ACO), three algorithms are proposed to construct rule-based classifiers to distinguish between non-spam and spam hosts. Moreover, the paper also proposes an adaptive learning technique to enhance the spam detection performance.

Design/methodology/approach

The Trust-ACO algorithm is designed to let an ant start from a non-spam seed, and afterwards, decide to walk through paths in the host graph. Trails (i.e. trust paths) discovered by ants are then interpreted and compiled to non-spam classification rules. Similarly, the Distrust-ACO algorithm is designed to generate spam classification ones. The last Combine-ACO algorithm aims to accumulate rules given from the former algorithms. Moreover, an adaptive learning technique is introduced to let ants walk with longer (or shorter) steps by rewarding them when they find desirable paths or penalizing them otherwise.

Findings

Experiments are conducted on two publicly available WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets. The results show that the proposed algorithms outperform well-known rule-based classification baselines. Especially, the proposed adaptive learning technique helps improving the AUC scores up to 0.899 and 0.784 on the former and the latter datasets, respectively.

Originality/value

To the best of our knowledge, this is the first comprehensive study that adopts the ACO learning approach to solve the problem of Web spam detection. In addition, we have improved the traditional ACO by using the adaptive learning technique.

Details

International Journal of Web Information Systems, vol. 11 no. 2
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 13 April 2022

Xiaofan Liu, Yupeng Zhou, Minghao Yin and Shuai Lv

The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and…

Abstract

Purpose

The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and well-known non-deterministic polynomial (NP)-hard problem whose goal is to cover at least k elements with as few subsets as possible.

Design/methodology/approach

In this work, the authors present a novel variant of the ant colony optimization (ACO) algorithm, called Argentine ant system (AAS), to deal with the PSCP. The developed AAS is an integrated system of different populations that use the same pheromone to communicate. Moreover, an effective local search framework with the relaxed configuration checking (RCC) and the volatilization-fixed weight mechanism is proposed to improve the exploitation of the algorithm.

Findings

A detailed experimental evaluation of 75 instances reveals that the proposed algorithm outperforms the competitors in terms of the quality of the optimal solutions. Also, the performance of AAS gradually improves with the growing instance size, which shows the potential in handling complex practical scenarios. Finally, the designed components of AAS are experimentally proved to be beneficial to the whole framework. Finally, the key components in AAS have been demonstrated.

Originality/value

At present, there is no heuristic method to solve this problem. The authors present the first implementation of heuristic algorithm for solving PSCP and provide competitive solutions.

Details

Data Technologies and Applications, vol. 56 no. 5
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 7 March 2008

A. Kaveh and P. Sharafi

Medians of a graph have many applications in engineering. Optimal locations for facility centers, distribution of centers and domain decomposition for parallel computation are a…

Abstract

Purpose

Medians of a graph have many applications in engineering. Optimal locations for facility centers, distribution of centers and domain decomposition for parallel computation are a few examples of such applications. In this paper, a new ant system (AS) algorithm based on the idea of using two sets of ants, named active and passive ants is proposed for the problem of finding k‐medians of a weighted graph or the facility location problem on a network.

Design/methodology/approach

The structure of the algorithm is derived from two known heuristics; namely, rank‐based AS and max‐min ant system with some adjustments in pheromone updating and locating the ants on the graph nodes. The algorithms are designed with and without a local search.

Findings

An efficient algorithm for location finding, and the novel application of an ant colony system can be considered as the main contribution of this paper.

Originality/value

Combining two different tools; namely, graph theory and AS algorithm results in an efficient and accurate method for location finding. The results are compared to those of another algorithm based on the theory of graphs.

Details

Engineering Computations, vol. 25 no. 2
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 21 August 2009

Jelmer Marinus van Ast, Robert Babuška and Bart De Schutter

The purpose of this paper is to propose a novel ant colony optimization (ACO) approach to optimal control. The standard ACO algorithms have proven to be very powerful optimization…

Abstract

Purpose

The purpose of this paper is to propose a novel ant colony optimization (ACO) approach to optimal control. The standard ACO algorithms have proven to be very powerful optimization metaheuristic for combinatorial optimization problems. They have been demonstrated to work well when applied to various nondeterministic polynomial‐complete problems, such as the travelling salesman problem. In this paper, ACO is reformulated as a model‐free learning algorithm and its properties are discussed.

Design/methodology/approach

First, it is described how quantizing the state space of a dynamic system introduces stochasticity in the state transitions and transforms the optimal control problem into a stochastic combinatorial optimization problem, motivating the ACO approach. The algorithm is presented and is applied to the time‐optimal swing‐up and stabilization of an underactuated pendulum. In particular, the effect of different numbers of ants on the performance of the algorithm is studied.

Findings

The simulations show that the algorithm finds good control policies reasonably fast. An increasing number of ants results in increasingly better policies. The simulations also show that although the policy converges, the ants keep on exploring the state space thereby capable of adapting to variations in the system dynamics.

Research limitations/implications

This paper introduces a novel ACO approach to optimal control and as such marks the starting point for more research of its properties. In particular, quantization issues must be studied in relation to the performance of the algorithm.

Originality/value

The paper presented is original as it presents the first application of ACO to optimal control problems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 2 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 28 March 2008

Nabil Nahas, Mustapha Nourelfath and Daoud Ait‐Kadi

The purpose of this paper is to extend the optimal design problem of series manufacturing production lines to series‐parallel lines, where redundant machines and in‐process…

Abstract

Purpose

The purpose of this paper is to extend the optimal design problem of series manufacturing production lines to series‐parallel lines, where redundant machines and in‐process buffers are both included to achieve a greater production rate. The objective is to maximize production rate subject to a total cost constraint.

Design/methodology/approach

An analytical method is proposed to evaluate the production rate, and an ant colony approach is developed to solve the problem. To estimate series‐parallel production line performance, each component (i.e. each set of parallel machines) of the original production line is approximated as a single unreliable machine. To determine the steady state behaviour of the resulting non‐homogeneous production line, it is first transformed into an approximately equivalent homogeneous line. Then, the well‐known Dallery‐David‐Xie algorithm (DDX) is used to solve the decomposition equations of the resulting (homogenous) line. The optimal design problem is formulated as a combinatorial optimisation one where the decision variables are buffers and types of machines, as well as the number of redundant machines. The effectiveness of the ant colony system approach is illustrated through numerical examples.

Findings

Simulation results show that the analytical approximation used to estimate series‐parallel production lines is very accurate. It has been found also that ant colonies can be extended to deal with the series‐parallel extension to determine near‐optimal or optimal solutions in a reasonable amount of time.

Practical implications

The model and the solution approach developed can be applied for optimal design of several industrial systems such as manufacturing lines and power production systems.

Originality/value

The paper presents an approach for the optimal design problem of series‐parallel manufacturing production lines.

Details

Journal of Quality in Maintenance Engineering, vol. 14 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 12 November 2013

Yancang Li, Chenguang Ban and Rouya Li

Ant colony algorithm is widely used in recent years as a heuristic algorithm. It provides a new way to solve complicated combinatorial optimization problems. Having been…

Abstract

Ant colony algorithm is widely used in recent years as a heuristic algorithm. It provides a new way to solve complicated combinatorial optimization problems. Having been enlightened by the behavior of ant colony's searching for food, positive feedback construction and distributed computing combined with certain heuristics are adopted in the algorithm, which makes it easier to find better solution. This paper introduces a series of ant colony algorithm and its improved algorithm of the basic principle, and discusses the ant colony algorithm application situation. Finally, several problems existing in the research and the development prospect of ACO are reviewed.

Details

World Journal of Engineering, vol. 10 no. 5
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 19 July 2018

Lahna Idres and Mohammed Said Radjef

Until now, the algorithms used to compute an equilibrate route assignment do not return an integer solution. This disagreement constitutes a non-negligible drawback. In fact, it…

Abstract

Purpose

Until now, the algorithms used to compute an equilibrate route assignment do not return an integer solution. This disagreement constitutes a non-negligible drawback. In fact, it is shown in the literature that a fractional solution is not a good approximation of the integer one. The purpose of this paper is to find an integer route assignment.

Design/methodology/approach

The static route assignment problem is modeled as an asymmetric network congestion game. Then, an algorithm inspired from ant supercolony behavior is constructed, in order to compute an approximation of the Pure Nash Equilibrium (PNE) of the considered game. Several variants of the algorithm, which differ by their initializing steps and/or the kind of the provided algorithm information, are proposed.

Findings

An evaluation of these variants over different networks is conduced and the obtained results are encouraging. Indeed, the adaptation of ant supercolony behavior to solve the problem under consideration shows interesting results, since most of the algorithm’s variants returned high-quality approximation of PNE in more than 91 percent of the treated networks.

Originality/value

The asymmetric network congestion game is used to model route assignment problem. An algorithm with several variants inspired from ant supercolony behavior is developed. Unlike the classical ant colony algorithms where there is one nest, herein, several nests are considered. The deposit pheromone of an ant from a given nest is useful for the ants of the other nests.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 11 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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