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1 – 10 of over 1000Phannakan Tengkiattrakul, Saranya Maneeroj and Atsuhiro Takasu
This paper aims to propose a trust-based ant-colony recommender system. It achieves high accuracy and coverage by integrating the importance level of friends. This paper has two…
Abstract
Purpose
This paper aims to propose a trust-based ant-colony recommender system. It achieves high accuracy and coverage by integrating the importance level of friends. This paper has two main contributions, namely, selecting higher-quality raters and improving the prediction step. From these two contributions, the proposed trust-based ant-colony recommender system could provide more accurate and wider-coverage prediction than existing systems.
Design/methodology/approach
To obtain higher-quality raters, the data set was preprocessed, and then, trust values were calculated. The depth of search was increased to obtain higher coverage levels. This work also focuses on the importance level of friends in the system. Because the levels of influence on the active user of all friends are not equal, the importance level of friends is integrated into the system by transposing rater’s rating to the active user’s perspective and then assigning a weight to each rater.
Findings
The experimental evaluation clearly demonstrates that the proposed method achieves better results in terms of both accuracy and coverage than existing trust-based recommender systems. It was found that integrating the importance level of friends into the system, which transposes ratings and assigns weight to each user, can increase accuracy and coverage.
Originality/value
Existing trust-based ant-colony recommender systems do not consider the importance level of friends in the prediction step. Most of them only focus on finding raters and then using the rater’s real ratings in the prediction step. A new method is proposed that integrates the importance level of friends into the system by transposing a rater’s rating to match the active user’s perspective and assigning a weight for each rater. The experimental evaluation demonstrates that the proposed method achieves better accuracy and coverage than existing systems.
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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.
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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.
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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.
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Frank Chiang, Robin Braun and John Hughes
This paper describes the design of a scalable bio‐mimetic framework that addresses several key issues of autonomous agents in the functional management domain of complex…
Abstract
This paper describes the design of a scalable bio‐mimetic framework that addresses several key issues of autonomous agents in the functional management domain of complex Ubiquitous Service‐Oriented Networks.We propose an autonomous network service management platform ‐ SwarmingNet, which is motivated by observations of the swarm intelligence in biological systems (e.g., Termite, Ant/Bees colonies, or Locusts ). In this SwarmingNet architecture, the required network service processes are implemented by a group of highly diverse and autonomic objects. These objects are called TeleService Solons (TSSs) as elements of TeleService Holons (TSHs), analoguous to individual insects as members of the whole colony. A single TSS is only able to pursue simple behaviors and interactions with local neighbors, on the contrary, a group of TSSs have the capabilities of fulfilling the complex tasks relating to service discovery and service activation.We simulate a service configuration process for a Multimedia Messaging Service, and a performance comparison between the bio‐agents and normal agents is analyzed. Finally, we conclude that through bio‐swarming intelligence behaviors, this infrastructure develops the enhanced self‐X capabilities which give IP networks advantages of instinctive compatibility, efficiency and scalability.
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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.
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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.
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Gopinath Anjinappa and Divakar Bangalore Prabhakar
The fluctuations that occurred between the power requirements have shown a higher range of voltage regulations and frequency. The fluctuations are caused because of substantial…
Abstract
Purpose
The fluctuations that occurred between the power requirements have shown a higher range of voltage regulations and frequency. The fluctuations are caused because of substantial changes in the energy dissipation. The operational efficiency has been reduced when the power grid is enabled with the help of electric vehicles (EVs) that were created by the power resources. The model showed an active load matching for regulating the power and there occurred a harmonic motion in energy. The main purpose of the proposed research is to handle the energy sources for stabilization which has increased the reliability and improved the power efficiency. This study or paper aims to elaborate the security and privacy challenges present in the vehicle 2 grid (V2G) network and their impact with grid resilience.
Design/methodology/approach
The smart framework is proposed which works based on Internet of Things and edge computations that managed to perform an effective V2G operation. Thus, an optimum model for scheduling the charge is designed on each EV to maximize the number of users and selecting the best EV using the proposed ant colony optimization (ACO). At the first, the constructive phase of ACO where the ants in the colony generate the feasible solutions. The constructive phase with local search generates an ACO algorithm that uses the heterogeneous colony of ants and finds effectively the best-known solutions widely to overcome the problem.
Findings
The results obtained by the existing in-circuit serial programming-plug-in electric vehicles model in terms of power usage ranged from 0.94 to 0.96 kWh which was lower when compared to the proposed ACO that showed power usage of 0.995 to 0.939 kWh, respectively, with time. The results showed that the energy aware routed with ACO provided feasible routing solutions for the source node that provided the sensor network at its lifetime and security at the time of authentication.
Originality/value
The proposed ACO is aware of energy routing protocol that has been analyzed and compared with the energy utilization with respect to the sensor area network which uses power resources effectively.
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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.
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Lei Wu, Xue Tian, Hongyan Wang, Qi Liu and Wensheng Xiao
As a kind of NP-hard combinatorial optimization problem, pipe routing design (PRD) is applied widely in modern industries. In the offshore oil and gas industry, a semi-submersible…
Abstract
Purpose
As a kind of NP-hard combinatorial optimization problem, pipe routing design (PRD) is applied widely in modern industries. In the offshore oil and gas industry, a semi-submersible production platform is an important equipment for oil exploitation and production. PRD is one of the most key parts of the design of semi-submersible platform. This study aims to present an improved ant colony algorithm (IACO) to address PRD for the oil and gas treatment system when designing a semi-submersible production platform.
Design/methodology/approach
First, to simplify PRD problem, a novel mathematical model is built according to real constraints and rules. Then, IACO, which combines modified heuristic function, mutation mechanism and dynamical parameter mechanism, is introduced.
Findings
Based on a set of specific instances, experiments are carried out, and the experimental results show that the performance of IACO is better than that of two variants of ACO, especially in terms of the convergence speed and swarm diversity. Finally, IACO is used to solve PRD for the oil and gas treatment system of semi-submersible production platform. The simulation results, which include nine pipe paths, demonstrate the practicality and high-efficiency of IACO.
Originality/value
The main contribution of this study is the development of method for solving PRD of a semi-submersible production platform based on the novel mathematical model and the proposed IACO.
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