Memory-efficient Internet Protocol (IP) lookup with high speed is essential to achieve link-speed packet forwarding in IP routers. The rapid growth of Internet traffic and the development of optical link technologies have made IP lookup a major performance bottleneck in core routers. In this paper, we propose a new IP route lookup architecture based on hardware called Prefix-Route Trie (PR-Trie), which supports both IPv4 and IPv6 addresses. In PR-Trie, we develop a novel structure called Overlapping Hybrid Trie (OHT) to perform fast longest-prefix-matching (LPM) based on Multibit-Trie (MT), and a hash-based level matching query used to achieve only one off-chip memory access per lookup. In addition, the proposed PR-Trie also supports fast incremental updates. Since the memory complexity in MT-based IP lookup schemes depends on the level-partitioning solution and the data structure used, we develop an optimization algorithm called Bitmap-based Prefix Partitioning Optimization (BP2O). The proposed BP2O is based on a heuristic search using Ant Colony Optimization (ACO) algorithms to optimize memory efficiency. Experimental results using real-life routing tables prove that our proposal has superior memory efficiency. Theoretical performance analyses show that PR-Trie outperforms the classical Trie-based IP lookup algorithms.

Feature selection is important for learning algorithms, and it is still an open problem. Antlion optimizer is an excellent nature inspired method, but it doesn't work well for feature selection. This paper proposes a hybrid approach called Ant-Antlion Optimizer which combines advantages of antlion's smart behavior of antlion optimizer and ant's powerful searching movement of ant colony optimization. A mutation operator is also adopted to strengthen exploration ability. Comprehensive experiments by binary classification problems show that the proposed algorithm is superiority to other state-of-art methods on four performance indicators.

Personalized trip planning is a challenging problem given that places of interest should be selected according to user preferences and sequentially arranged while satisfying various constraints. In this study, we aimed to model various uncertain aspects that should be considered during trip planning and efficiently generate personalized plans that maximize user satisfaction based on preferences and constraints. Specifically, we propose a probabilistic itinerary evaluation model based on a hybrid temporal Bayesian network that determines suitable itineraries considering preferences, constraints, and uncertain environmental variables. The model retrieves the sum of time-weighted user satisfaction, and ant colony optimization generates the trip plan that maximizes the objective function. First, the optimization algorithm generates candidate itineraries and evaluates them using the proposed model. Then, we improve candidate itineraries based on the evaluation results of previous itineraries. To validate the proposed trip planning approach, we conducted an extensive user study by asking participants to choose their preferred trip plans from options created by a human planner and our approach. The results show that our approach provides human-like trip plans, as participants selected our generated plans in 57% of the pairs. We also evaluated the efficiency of the employed ant colony optimization algorithm for trip planning by performance comparisons with other optimization methods.

Most existing truth discovery approaches are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data for its unique characteristics such as multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). As for text answers, there are no absolute correctness or errors, most answers may be partially correct, which is quite different from the situation of traditional truth discovery. To solve these challenges, we propose an optimization-based text truth discovery model which jointly groups keywords extracted from the answers of the specific question into a set of multiple factors. Then, we select the subset of multiple factors as identified truth set for each question by parallel ant colony synchronization optimization algorithm. After that, the answers to each question can be ranked based on the similarities between factors answer provided and identified truth factors. The experiment results on real dataset show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art approaches.

Machine learning algorithms are becoming more and more popular in current era. Data preprocessing especially feature selection is helpful for improving the performance of those algorithms. A new powerful feature selection algorithm is proposed. It combines the advantages of ant colony optimization and brain storm optimization which simulates the behavior of human beings. Six classical datasets and five state-of-art algorithms are used to make a comparison with our algorithm on binary classification problems. The results on accuracy, percent rate, recall rate, and F1 measures show that the developed algorithm is more excellent. Besides, it is no more complex than the compared approaches.

In this paper, we propose a novel method for the design of CSD (Canonic Signed Digit) coefficient FIR (Finite Impulse Response) filters based on ACO (Ant Colony Optimization). This design problem is formulated as a combinatorial optimization problem and requires high computation time to obtain the optimal solution. Therefore, we propose an ACO approach for the design of CSD coefficient FIR filters. ACO is one of the promising approaches and appropriate for solving a combinatorial optimization problem in reasonable computation time. Several design examples showed the effectiveness of our method.

A huge amount of content exists on the Internet, and contents from mobile devices are also present. Growth of the Internet of Things (IoT) is further accelerating this trend. Content oriented networks have been proposed as a new network architecture that conducts routing using the content's ID instead of an IP address. Content queries are routed on the content name itself instead of a destination address in these content oriented networks. When the content from a mobile device moves somewhere else, all the routing tables are generally re-created with the movement information that the mobile device sends. However, a routing scheme that uses ant colony optimization has attracted attention for supporting this process, but this optimization has a problem in that it cannot cope with moving contents and users sufficiently. In this paper, we propose a scheme that can cope with moving contents sources and users that require contents by using pheromones that are laid by these moving mobile devices. This proposed scheme can be applied to case of not only moving content sources but also the moving request users. Moreover, we conduct simulations to evaluate the performance of the proposed scheme.

It is very difficult to know evolution state of ACO in its working. To solve the problem, we propose using colony entropy and mean colony entropy to monitor the algorithm. The two functions show fluctuation and declining trends depended on time t in a tour and iteration number. According to the principle, that each updated edge will get the same increment is improper. Then a weighted algorithm is proposed to calculate each arc's increment based on its selected probability. The strategy can provide more exploration to help to find the global optimum value, and experiments show its improved performance.

Metaheuristic methods have been studied for combinational optimization problems for some time. Recently, a Consultant-Guided Search (CGS) has been proposed as a metaheuristic method for the Traveling Salesperson Problem (TSP). This approach is an algorithm in which a virtual person called a client creates a solution based on consultation with a virtual person called a consultant. In this research, we propose a parallel algorithm which uses the Ant Colony System (ACS) to create a solution with a consultant in a Consultant-Guided Search, and calculate an approximation solution for the TSP. Finally, we execute a computer experiment using the benchmark problems (TSPLIB). Our algorithm provides a solution with less than 2% error rate for problem instances using less than 2000 cities.

The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics.

Ant Colony Optimization (ACO) is one of the most recent techniques for solving combinatorial optimization problems, and has been unexpectedly successful. Therefore, many improvements have been proposed to improve the performance of the ACO algorithm. In this paper an ant colony optimization with memory is proposed, which is applied to the classical traveling salesman problem (TSP). In the proposed algorithm, each ant searches the solution not only according to the pheromone and heuristic information but also based on the memory which is from the solution of the last iteration. A large number of simulation runs are performed, and simulation results illustrate that the proposed algorithm performs better than the compared algorithms.

The dynamic channel allocation (DCA) scheme in multi-cell systems causes serious inter-cell interference (ICI) problem to some existing calls when channels for new calls are allocated. Such a problem can be addressed by advanced centralized DCA design that is able to minimize ICI. Thus, in this paper, a centralized DCA is developed for the downlink of multi-cell orthogonal frequency division multiple access (OFDMA) systems with full spectral reuse. However, in practice, as the search space of channel assignment for centralized DCA scheme in multi-cell systems grows exponentially with the increase of the number of required calls, channels, and cells, it becomes an NP-hard problem and is currently too complicated to find an optimum channel allocation. In this paper, we propose an ant colony optimization (ACO) based DCA scheme using a low-complexity ACO algorithm which is a kind of heuristic algorithm in order to solve the aforementioned problem. Simulation results demonstrate significant performance improvements compared to the existing schemes in terms of the grade of service (GoS) performance and the forced termination probability of existing calls without degrading the system performance of the average throughput.

Ant Colony Optimization (ACO) algorithms are a new branch of swarm intelligence. They have been applied to solve different combinatorial optimization problems successfully. Their performance is very promising when they solve small problem instances. However, the algorithms' time complexity increase and solution quality decrease for large problem instances. So, it is crucial to reduce the time requirement and at the same time to increase the solution quality for solving large combinatorial optimization problems by the ACO algorithms. This paper introduces a Local Search based ACO algorithm (LSACO), a new algorithm to solve large combinatorial optimization problems. The basis of LSACO is to apply an adaptive local search method to improve the solution quality. This local search automatically determines the number of edges to exchange during the execution of the algorithm. LSACO also applies pheromone updating rule and constructs solutions in a new way so as to decrease the convergence time. The performance of LSACO has been evaluated on a number of benchmark combinatorial optimization problems and results are compared with several existing ACO algorithms. Experimental results show that LSACO is able to produce good quality solutions with a higher rate of convergence for most of the problems.

Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can evolve by performing genetic operation, and the balance between intensification and diversification can be adjusted by numbers of ants which perform genetic operation. The proposed algorithm is tested by simulating the Traveling Salesman Problem (TSP). Experimental studies show that the proposed ACO algorithm with genetic operation has superior performance when compared to other existing ACO algorithms.

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.