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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.

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.

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.

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.

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.

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.

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.

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.

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

In client/server distributed systems, the server is often the bottleneck. Improving the server performance is thus crucial for improving the overall performance of distributed information systems. Real‐time system is required to complete its work and deliver its services on a timely basis. The purpose of this paper is to propose a new scheduling algorithm for real‐time distributed system (client/server model) to achieve the above‐mentioned goal.

The ant colony optimization (ACO) algorithms are computational models inspired by the collective foraging behavior of ants. They provide inherent parallelism and robustness. Therefore, they are appropriate for scheduling of tasks in soft real‐time systems. During simulation, results are obtained with periodic tasks, measured in terms of success ratio and effective CPU utilization; and compared with results of earliest deadline first (EDF) algorithm in the same environment.

Analysis and experiments show that the proposed algorithm is equally efficient during underloaded conditions. The performance of EDF decreases as the load increases, but the proposed algorithm works well in overloaded conditions also. Because of this type of property, the proposed algorithm is more suitable for the situation when future workload of the system is unpredictable.

The application of ACO algorithms for scheduling of client/server real‐time distributed system, never found before in the literature. The new concept proposed in this paper will be of great significance to both theoretical and practical research in scheduling of distributed systems in the years to come.

The purpose of this paper is to present a graph representation of the design space that is suitable for the ant colony optimization (ACO) method in topology optimization (TO) problems.

The ACO is employed to obtain optimal routes in an equivalent graph representation of the discretized design space, with each route corresponding to a given distribution of material.

The problem associated with the maximization of the torque of a c‐core magnetic actuator is investigated, in which part of the yoke is discretized into a 16×8 grid and can assume three different materials: air, pure iron and a magnetic material.

The results of the c‐core magnetic actuator problem, which are in agreement with literature available, show the adequacy of the proposed approach to TO of electromagnetic devices.

The graph representation of the design space permits the solution of topological design problems with an arbitrary number of materials.

The results illustrate the potential of the methodology in dealing with multi‐domain TO problems, and the possibility to extend the application to 3D problems.

In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.

Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.

Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.

The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static.

The search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review.

The findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation.

The review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area.

Biomimicry is an interdisciplinary approach inspired by the living beings in nature while searching for solutions to solve mankind’s problems. This new approach emerging in the late 1990s has been quite innovative while dealing with basic problem solving processes in a business environment. Biomimicry is a creative solution for such processes as design, transformation, organization and sustainability in business enterprises. The objective of this work is to offer model samples that build a bridge between the nature and business organizations. The principles in nature offer many strategies for a sustainable business performance and thus help us maintain optimization and effectiveness in business management through cooperation.

Biogeography‐based optimization algorithm is a new kind of optimization algorithm based on biogeography. It is designed based on the migration strategy of animals to solve the problem of optimization. The purpose of this paper is to present a new algorithm – biogeography migration algorithm for traveling salesman problem (TSPBMA). A new special migration operator is designed for producing new solutions.

The paper gives the definition of TSP and models of TSPBMA; introduces the algorithm of TSPBMA in detail and gives the proof of convergence in theory; provides simulation results of TSPBMA compared with other optimization algorithms for TSP and presents some concluding remarks and suggestions for further work.

The TSPBMA is tested on some classical TSP problems. The comparison results with the other nature‐inspired optimization algorithms show that TSPBMA is useful for TSP combination optimization. Especially, the designed migration operator is very effective for TSP solving. Although the proposed TSPBMA is not better than ant colony algorithm in the respect of convergence speed and accuracy, it provides a new way for this kind of problem.

The migration operator is a new strategy for solving TSPs. It has never been used by any other evolutionary algorithm or swarm intelligence before TSPBMA.

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.

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.

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.

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.

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

The purpose of this paper is to propose a new population-based metaheuristic optimization algorithm, assimilation-accommodation mixed continuous ant colony optimization (ACACO), to improve the accuracy of Takagi-Sugeno-Kang-type fuzzy systems design.

The original N solution vectors in ACACO are sorted and categorized into three groups according to their ranks. The Research Learning scheme provides the local search capability for the best-ranked group. The Basic Learning scheme uses the ant colony optimization (ACO) technique for the worst-ranked group to approach the best solution. The operations of assimilation, accommodation, and mutation in Mutual Learning scheme are used for the middle-ranked group to exchange and accommodate the partial information between groups and, globally, search information. Only the N top-best-performance solutions are reserved after each iteration of learning.

The proposed algorithm outperforms some reported ACO algorithms for the fuzzy system design with the same number of rules. The performance comparison with various previously published neural fuzzy systems also shows its superiority even with a smaller number of fuzzy rules to those neural fuzzy systems.

Future work will consider the application of the proposed ACACO to the recurrent fuzzy network.

The originality of this work is to mix the work of the well-known psychologist Jean Piaget and the continuous ACO to propose a new population-based optimization algorithm whose superiority is demonstrated.