The majority of optimization algorithms require proper parameter tuning to achieve the best performance. However, it is well-known that parameters are problem-dependent as different problems or even ...different instances have different optimal parameter settings. Parameter tuning through the testing of parameter combinations is a computationally expensive procedure that is infeasible on large-scale real-world problems. One method to mitigate this is to introduce adaptivity into the algorithm to discover good parameter settings during the search. Therefore, this study introduces an adaptive approach to a heterogeneous ant colony population that evolves the alpha and beta controlling parameters for ant colony optimization (ACO) to locate near-optimal solutions. This is achievable by introducing a set of rules for parameter adaptation to occur in order for the parameter values to be close to the optimal values by exploring and exploiting both the parameter and fitness landscape during the search to reflect the dynamic nature of search. In addition, the 3-opt local search heuristic is integrated into the proposed approach to further improve fitness. An empirical analysis of the proposed algorithm tested on a range of Travelling Salesman Problem (TSP) instances shows that the approach has better algorithmic performance when compared against state-of-the-art algorithms from the literature.
•A heterogeneous adaptive ACO with 3 opt is proposed to tackle the Travelling Salesman Problem.•The alpha and beta values of best performing ant were mutated and adapted by the worst performing ant.•Best and worst ant chosen over every 5 iterations for the adaptivity approach.•Gaussian mutation is used to create offspring that are much closer to the genes of the parents.•Homogeneous ants used in first 5 iterations lead heterogeneous ants to better starting solutions.
•A probabilistic optimization framework incorporated with uncertainty is proposed.•A hybrid optimization approach combining ACO and ABC algorithms is proposed.•The problem is to deal with technical, ...environmental and economical aspects.•A fuzzy interactive approach is incorporated to solve the multi-objective problem.•Several strategies are implemented to compare with literature methods.
In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods.
Due to their strong risk tolerance, low manufacturing cost, and good maneuverability, unmanned aerial vehicles (UAVs) have been widely used in various fields. Among related challenges, coordinated ...task assignment is a key scientific issue for autonomous control of UAVs. In this article, based on the idea of fuzzy C-means clustering and the ant colony optimization algorithm, a cooperative multiple task reallocation problem with target precedence constraints for heterogeneous UAVs is proposed. The contributions of this research are the performance evaluation of the original algorithms in a dynamic context, consideration of changes in some attributes of the environment, and the extension of these algorithms to properly address more realistic dynamic emergent adjustment scenarios. According to the corresponding task reallocation strategy, the scenarios are divided into three categories: the complete redistribution strategy can effectively cope with scenarios where tasks have changed significantly, the partial adjustment strategy can induce partial responses to the changes of individual tasks, and group redistribution can effectively solve the problem of task target threat rating changes. The simulation results show that the dynamic reallocation model of multi-UAV tasks in dynamic emergent adjustment scenarios can achieve better performance to complete the corresponding tasks based on the proposed scheme. In addition, we deployed the developed graphical modeling and analysis software platform to implement the dynamic reallocation model of multi-UAV tasks in dynamic emergent scenarios, and the validity and reliability of the proposed task reallocation model were verified.
Developing highly efficient routing protocols for vehicular ad hoc networks (VANETs) is a challenging task, mainly due to the special characters of such networks: large-scale sizes, frequent link ...disconnections, and rapid topology changes. In this paper, we propose an adaptive quality-of-service (QoS)-based routing for VANETs called AQRV. This new routing protocol adaptively chooses the intersections through which data packets pass to reach the destination, and the selected route should satisfy the QoS constraints and fulfil the best QoS in terms of three metrics, namely connectivity probability, packet delivery ratio (PDR), and delay. To achieve the given objectives, we mathematically formulate the routing selection issue as a constrained optimization problem and propose an ant colony optimization (ACO)-based algorithm to solve this problem. In addition, a terminal intersection (TI) concept is presented to decrease routing exploration time and alleviate network congestion. Moreover, to decrease network overhead, we propose local QoS models (LQMs) to estimate real time and complete QoS of urban road segments. Simulation results validate our derived LQM models and show the effectiveness of AQRV.
In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony ...optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.
Recently, research on path planning for the autonomous underwater vehicles (AUVs) has developed rapidly. Heuristic algorithms have been widely used to plan a path for AUV, but most traditional ...heuristic algorithms are facing two problems, one is slow convergence speed, the other is premature convergence. To solve the above problems, this paper proposes a new heuristic algorithms fusion, which improves the genetic algorithm with the ant colony optimization algorithm and the simulated annealing algorithm. In addition, to accelerate convergence and expand the search space of the algorithm, some algorithms like trying to cross, path self-smoothing and probability of genetic operation adjust adaptively are proposed. The advantages of the proposed algorithm are reflected through simulated comparative experiments. Besides, this paper proposes an ocean current model and a kinematics model to solve the problem of AUV path planning under the influence of ocean currents.
Many sampling-based preprocessing methods have been proposed to solve the problem of unbalanced dataset classification. The fundamental principle of these methods is rebalancing an unbalanced dataset ...by a concrete strategy. Herein, we introduce a novel hybrid proposal named ant colony optimization resampling (ACOR) to overcome class imbalance classification. ACOR primarily includes two steps: first, it rebalances an imbalanced dataset by a specific oversampling algorithm; next, it finds an (sub)optimal subset from the balanced dataset by ant colony optimization. Unlike other oversampling techniques, ACOR does not focus on the mechanics of generating new samples. The main advantage of ACOR is that existing oversampling algorithms can be fully utilized and an ideal training set can be obtained by ant colony optimization. Therefore, ACOR can enhance the performance of existing oversampling algorithms. Experimental results on 18 real imbalanced datasets prove that ACOR yields significantly better results compared with four popular oversampling methods in terms of various assessment metrics, such as AUC, G-mean, and BACC.
•ACOR rebalances an imbalanced dataset by a specific oversampling algorithm.•ACOR does not focus on the mechanics of generating new samples.•ACOR can enhance the performance of existing oversampling algorithms.
To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization ...problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.
Although the continuous version of ant colony optimizer (ACOR) has been successfully applied to various problems, there is room to boost its stability and improve convergence speed and precision. In ...addition, it is prone to stagnation, which means it cannot step out of the local optimum (LO). To effectively mitigate these concerns, an improved method using a random spare strategy and chaotic intensification strategy is proposed. Also, its selection mechanism is enhanced in our research. Among the new components, the convergence speed is mainly boosted by using a random spare approach. To effectively augment the ability to step out of LO and to refine the convergence accuracy, the chaotic intensification strategy and improved selection mechanism are applied to ACOR. To better verify the effectiveness of the proposed method, a series of comparative experiments are conducted by using 30 benchmark functions. According to all experimental results, it is evident that the convergence rapidity and accuracy of the proposed method is better than other peers. In addition, it is observed that the capability of enhanced RCACO is more reliable than other techniques in stepping out of LO. Furthermore, an excellent multi-threshold image segmentation method is proposed in this paper. On this basis, image segmentation experiments at low threshold levels and high threshold levels are also respectively carried out. The experimental results also adequately disclose that the segmentation results of RCACO for both multi-threshold image segmentation at a low threshold level and high threshold level, are even more satisfactory compared to other studied algorithms. An online homepage supports this research for access to sharable codes, any question and info about this research at https://aliasgharheidari.com.
Feature selection (FS) has received significant attention since the use of a well-selected subset of features may achieve better classification performance than that of full features in many ...real-world applications. It can be considered as a multiobjective optimization consisting of two objectives: 1) minimizing the number of selected features and 2) maximizing classification performance. Ant colony optimization (ACO) has shown its effectiveness in FS due to its problem-guided search operator and flexible graph representation. However, there lacks an effective ACO-based approach for multiobjective FS to handle the problematic characteristics originated from the feature interactions and highly discontinuous Pareto fronts. This article presents an Information-theory-based Nondominated Sorting ACO (called INSA) to solve the aforementioned difficulties. First, the probabilistic function in ACO is modified based on the information theory to identify the importance of features; second, a new ACO strategy is designed to construct solutions; and third, a novel pheromone updating strategy is devised to ensure the high diversity of tradeoff solutions. INSA's performance is compared with four machine-learning-based methods, four representative single-objective evolutionary algorithms, and six state-of-the-art multiobjective ones on 13 benchmark classification datasets, which consist of both low and high-dimensional samples. The empirical results verify that INSA is able to obtain solutions with better classification performance using features whose count is similar to or less than those obtained by its peers.
