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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although they are approximate methods (i.e. their solution are good, but not provably optimal), they do not require the derivatives of the objective function and constraints. Also, they use probabilistic transition rules instead of deterministic rules. The purpose of this paper is to present an improved ant colony optimization (IACO) for constrained engineering design problems.

IACO has the capacity to handle continuous and discrete problems by using sub‐optimization mechanism (SOM). SOM is based on the principles of finite element method working as a search‐space updating technique. Also, SOM can reduce the size of pheromone matrices, decision vectors and the number of evaluations. Though IACO decreases pheromone updating operations as well as optimization time, the probability of finding an optimum solution is not reduced.

Utilizing SOM in the ACO algorithm causes a decrease in the size of the pheromone vectors, size of the decision vector, size of the search space, the number of function evaluations, and finally the required optimization time. SOM performs as a search‐space‐updating rule, and it can exchange discrete‐continuous search domain to each other.

The suitability of using ACO for constrained engineering design problems is presented, and applied to optimal design of different engineering problems.

The purpose of this paper is to show that the theoretical proofs of convergence in solution of ant colony optimization (ACO) algorithms have significant values of theory and application.

This paper adapts the basic ACO algorithm framework and proves two important ACO subclass algorithms which are ACO_bs,τmin and ACO_{bs,τmin (t)}.

This paper indicates that when the minimums of pheromone trial decay to 0 with the speed of logarithms, it is ensured that algorithms can, at least, get a certain optimal solution. Even if the randomicity and deflection of random algorithms are disturbed infinitesimally, algorithms can obtain optimal solution.

This paper focuses on the analysis and proof of the convergence theory of ACO subset algorithm to explore internal mechanism of ACO algorithm.

The problem of estimating the minimum forces extracted by robot fingers on the surface of a grasped rigid object is very crucial to guarantee the stability of the grip without causing defect or damage to the grasped object. Solving this problem is investigated in this paper. Moreover, the optimum sets of parameters used to tune the algorithm are also studied here.

Ant Colony Optimization (ACO), which is a swarm intelligence‐based method, is used in this work to solve this problem. The problem under scope is a complex, constraint optimization problem. We develop our own approach to calculate those minimum forces. Ants ability to reorganize and behave collectively is modelled here. The required forces are a result of the final ants distribution around the fingers contact points. Ants move from contact point to another following the maximum pheromone level direction until they settle on a solution that accomplishes the given criteria. Ants number on a contact point constitutes the total force exerted by a finger on that contact point. The process is repeated until optimum solution is found. Simulations are repeated to track down most suitable ACO parameters for this type of problems and with different fingers configurations.

The results show that ACO can find optimum fingers forces for grasping rigid objects. These objects could be any polygon with or without friction between the fingers tips and the object surface. The method is computationally acceptable and can be applied with different fingers configurations and with different friction coefficients. We found that the optimal set of parameters used to tune ACO is independent of the initial number of ants on each location.

In this paper we present a very original, new, and interesting technique used to solve the optimum grasping forces of rigid objects. It is a well‐known fact that standard optimization techniques have their own requirements and limitations. This technique is based on swarm intelligence. This work opens the door for further investigations on how nature based methods can be used to solve complex problems. ACO offers a simple, yet structure approach to solve nonlinear constraint optimization problems.

This paper aims to describe a new hybrid ant colony optimization (ACO) algorithm developed to solve facility layout problems (FLPs) formulated as quadratic assignment problems (QAPs).

A hybrid ACO algorithm which combines max‐min ant system (MMAS) (i.e. a variant of ACO) with genetic algorithm (GA) has been developed. The hybrid algorithm is further improved with the use of a novel minimum pheromone threshold strategy (MPTS).

The hybrid algorithm shows satisfactory results in the experimental evaluation due to the synergy and collaboration between MMAS and GA. The results also show that the use of MPTS helps them to achieve such performance, by promoting search diversification.

The experimental evaluation presented emphasizes more on the search performance or pattern of the hybrid algorithm. Detailed computational work could reveal other strengths of the algorithm.

The developmental work presented in this paper could be used by researchers and practitioners to solve QAPs. Its use may also be expanded to solve other combinatorial optimization and engineering problems.

This paper provides useful insights into the development of a hybrid ACO algorithm that combines MMAS with GA for solving QAPs.

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.

A new algorithm based on ant colony optimization (ACO) for data clustering has been developed.

ACO technique along with simulated annealing, tournament selection (GA), Tabu search and density distribution are used to solve unsupervised clustering problem for making similar groups from arbitrarily entered large data.

Distinctive clusters of similar data are formed metaheuritically from arbitrarily entered mixed data based on similar attributes of data.

The authors have run a computer program for a number of cases related to data clustering. So far, there are no problems in convergence of results for formation of distinctive similar groups with given data set quickly and accurately.

ACO‐based method developed here can be applied to practical industrial problems for mobile robotic navigation other than data clustering and travelling salesman.

This paper will enable the solving of problems related to mixed data, which requires the formation of a number of groups of similar data without having a prior knowledge of divisions, which lead to unbiased clustering. The computer code developed in this work is based on a metaheuristic algorithm and presented here to solve a number of cases.