Performance Of Ant Colony Optimization Research Articles

Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery.

Ant colony optimization (ACO) algorithm is widely used in the instant delivery order scheduling because of its distributed computing capability. However, the order delivery efficiency decreases when different logistics statuses are faced. In order to improve the performance of ACO, an adaptive ACO algorithm based on real-time logistics features (AACO-RTLFs) is proposed. First, features are extracted from the event dimension, spatial dimension, and time dimension of the instant delivery to describe the real-time logistics status. Five key factors are further selected from the above three features to assist in problem modeling and ACO designing. Second, an adaptive instant delivery model is built considering the customer's acceptable delivery time. The acceptable time is calculated by emergency order mark and weather conditions in the event dimension feature. Third, an adaptive ACO algorithm is proposed to obtain the instant delivery order schedules. The parameters of the probability equation in ACO are adjusted according to the extracted key factors. Finally, the Gurobi solver in Python is used to perform numerical experiments on the classical datasets to verify the effectiveness of the instant delivery model. The proposed AACO-RTLF algorithm shows its advantages in instant delivery order scheduling when compared to the other state-of-the-art algorithms.

Focused ACO with node relocation procedure for solving large TSP instances

Ant Colony Optimization (ACO) is a well-known family of nature-inspired metaheuristics used to find approximate solutions for challenging optimization problems. Despite its relative simplicity and adaptability to new problem variants, the efficiency of ACO significantly deteriorates with increasing problem size, even though it often provides high-quality solutions. This paper aims to enhance the performance of ACO when solving large instances of the Traveling Salesman Problem (TSP). Specifically, we propose novel improvements to the current state-of-the-art ACO approach, Focused ACO (FACO), that include a node relocation procedure applied during the solution construction process and a greedy heuristic that preserves high-quality solutions from previous algorithm iterations. Our proposed FACO variants demonstrate a substantial performance improvement when solving TSP instances with up to 200k nodes.

A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

The dynamic traveling salesman problem (DTSP) is significant in logistics distribution in real-world applications in smart cities, but it is uncertain and difficult to solve. This paper proposes a scheme library-based ant colony optimization (ACO) with a two-optimization (2-opt) strategy to solve the DTSP efficiently. The work is novel and contributes to three aspects: problem model, optimization framework, and algorithm design. Firstly, in the problem model, traditional DTSP models often consider the change of travel distance between two nodes over time, while this paper focuses on a special DTSP model in that the node locations change dynamically over time. Secondly, in the optimization framework, the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment. The framework of offline optimization and online application is proposed due to the fact that the environmental change in DTSP is caused by the change of node location, and therefore the new environment is somehow similar to certain previous environments. This way, in the offline optimization, the solutions for possible environmental changes are optimized in advance, and are stored in a mode scheme library. In the online application, when an environmental change is detected, the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity. Thirdly, in the algorithm design, the ACO cooperates with the 2-opt strategy to enhance search efficiency. To evaluate the performance of ACO with 2-opt, we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms. The experimental results show that ACO with 2-opt can solve the DTSPs effectively.

Heuristic smoothing ant colony optimization with differential information for the traveling salesman problem

The traveling salesman problem (TSP) is an NP complete problem with potential applications. Thus far, numerous approaches have been proposed to solve the TSP including exact, heuristic and metaheuristic methods. Among them, the ant colony optimization (ACO) algorithm, belonging to metaheuristic methods, has proven to be an efficient method for solving the TSP. However, the phenomena of rapid convergence to local optima and unsatisfactory computational accuracy distinctly limit the performance of ACO. This study therefore proposes a novel ACO algorithm (HSDACO) to compensate for these shortcomings. In the HSDACO algorithm, the involvement of heterogeneous population automation at each iteration generates better candidate solutions with maintaining parameter diversity. Then, the implementation of three smoothing techniques with the 2-Opt method guarantees the effective assessment of candidate solutions in favor of higher quality. Next, the introduction of a differential information updating mechanism, using differential edge information obtained from the candidate solutions, enables the evaporation operation of the pheromone trail to obtain more reasonable guidance. Subsequently, the design of evolutionary state estimation and adjustment is used to monitor the population under correct guidance and regulate the evolutionary state effectively and efficiently. Finally, the HSDACO algorithm is evaluated on public TSP benchmark instances and compared with the other state-of-the-art algorithms. Experimental results show that the proposed HSDACO achieves substantial improvement for mid-scale TSP instances and overwhelming advantages for small-scale TSP instances, in terms of solution accuracy and convergence speed.

Evaluation of PSO Algorithm Considering Obstacle Avoidance in Evacuation Guidance

In recent years, IoT has been expected to provide support during natural disasters, and studies focusing on ant colony optimization (ACO) have been conducted for providing evacuation routes for evacuees.
 We previously proposed a modified algorithm for ACO that improved on the slow convergence of ACO, but the problem with ACO-based evacuation is the time it takes the evacuees to reach a safe zone.
 
 In this study, we proposed a route suggestion algorithm that improves particle swarm optimization (PSO) to reduce the time required for ACO evacuation, and compared the performance of ACO and the proposed PSO. We also proposed a method that combines ACO and PSO and evaluated its performance.

Boosting ant colony optimization via solution prediction and machine learning

This paper introduces an enhanced meta-heuristic (ML-ACO) that combines machine learning (ML) and ant colony optimization (ACO) to solve combinatorial optimization problems. To illustrate the underlying mechanism of our ML-ACO algorithm, we start by describing a test problem, the orienteering problem. In this problem, the objective is to find a route that visits a subset of vertices in a graph within a time budget to maximize the collected score. In the first phase of our ML-ACO algorithm, an ML model is trained using a set of small problem instances where the optimal solution is known. Specifically, classification models are used to classify an edge as being part of the optimal route, or not, using problem-specific features and statistical measures. The trained model is then used to predict the ‘probability’ that an edge in the graph of a test problem instance belongs to the corresponding optimal route. In the second phase, we incorporate the predicted probabilities into the ACO component of our algorithm, i.e., using the probability values as heuristic weights or to warm start the pheromone matrix. Here, the probability values bias sampling towards favoring those predicted ‘high-quality’ edges when constructing feasible routes. We have tested multiple classification models including graph neural networks, logistic regression and support vector machines, and the experimental results show that our solution prediction approach consistently boosts the performance of ACO. Further, we empirically show that our ML model trained on small synthetic instances generalizes well to large synthetic and real-world instances. Our approach integrating ML with a meta-heuristic is generic and can be applied to a wide range of optimization problems.

Ant colony optimization for traveling salesman problem based on parameters optimization

Traveling salesman problem (TSP) is one typical combinatorial optimization problem. Ant colony optimization (ACO) is useful for solving discrete optimization problems whereas the performance of ACO depends on the values of parameters. The hybrid symbiotic organisms search (SOS) and ACO algorithm (SOS–ACO) is proposed for TSP. After certain parameters of ACO are assigned, the remaining parameters can be adaptively optimized by SOS. Using the optimized parameters, ACO finds the optimal or near-optimal solution and the complexity for assigning ACO parameters is greatly reduced. In addition, one simple local optimization strategy is incorporated into SOS–ACO for improving the convergence rate and solution quality. SOS–ACO is tested with different TSP instances in TSPLIB. The best solutions are within 2.33% of the known optimal solution. Compared with some of the previous algorithms, SOS–ACO finds the better solutions under the same preconditions. Finally, the performance of SOS–ACO is analyzed according to the changes of some ACO parameters. The experimental results illustrate that SOS–ACO has good adaptive ability to various values of these parameters for finding the competitive solutions.

An improved construction approach using ant colony optimization for solving the dynamic facility layout problem

This paper presents an improved Ant Colony Optimization (ACOII) algorithm to solve the dynamic facility layout problem for construction sites. The algorithm uses a construction approach in building the layout solutions over time and uses a discrete dynamic search with heuristic info based on both relocation and flow costs to influence facilities’ placement in different time periods. The performance of ACOII is investigated using randomly generated data sets where the number of facilities and the number of time periods in the planning horizon vary to mimic what happens on a construction site over time. The experimental results show that ACOII is effective in solving the problem. A benchmarking study using instances from the literature showed promising results with improved solutions for all instances with very large number of facilities and periods.

A Novel Service Composition Optimization Strategy

In order to cope with the increasingly complex application requirements, sensors work in a cooperative and collective manner. Service-oriented Wireless Sensor Networks (WSNs)frameworkbecomes an effective strategy to solve the cooperative work of sensors. Aiming at the key service composition optimization problem under this framework, this paper proposes a novel multi-channel directed harmony search algorithm (MDHS) algorithm after analyzing the performance of ant colony optimization (ACO), genetic algorithm (GA) and particle swarm optimization (PSO) algorithms on this problem. Based on harmony search algorithm (HS) algorithm, this algorithm adopts multi-channel, dynamic parameters, directional perturbation and other technologies, which greatly improves the excellence of service composition and solution efficiency. Extensive experiments have been carried out and the results show that our technique performs better than GA and PSO on reducing energy consumption and extending the life of the wireless sensor network.

Opposition-Based Ant Colony Optimization Algorithm for the Traveling Salesman Problem

Opposition-based learning (OBL) has been widely used to improve many swarm intelligent optimization (SI) algorithms for continuous problems during the past few decades. When the SI optimization algorithms apply OBL to solve discrete problems, the construction and utilization of the opposite solution is the key issue. Ant colony optimization (ACO) generally used to solve combinatorial optimization problems is a kind of classical SI optimization algorithm. Opposition-based ACO which is combined in OBL is proposed to solve the symmetric traveling salesman problem (TSP) in this paper. Two strategies for constructing opposite path by OBL based on solution characteristics of TSP are also proposed. Then, in order to use information of opposite path to improve the performance of ACO, three different strategies, direction, indirection, and random methods, mentioned for pheromone update rules are discussed individually. According to the construction of the inverse solution and the way of using it in pheromone updating, three kinds of improved ant colony algorithms are proposed. To verify the feasibility and effectiveness of strategies, two kinds of ACO algorithms are employed to solve TSP instances. The results demonstrate that the performance of opposition-based ACO is better than that of ACO without OBL.

A Comprehensive Analysis of ACO for Wireless Sensor Network Routing

The proliferations of IoT technologies and applications have led to an increased interest in Wireless Sensor Networks (and in particular, multi-hop networks). Wireless sensor networks are composed of small mobile terminals which have limited system resources. Due to this, these networks are vulnerable to changes in network status arising from changes in the network parameters such as, position / layout of sensors, signal strength, environmental conditions, etc. In addition, the network nodes are also constrained in terms of energy provided by the battery. It is an significant consideration to be accounted so as to prolong their operational time, since this adds to the network lifetime. Lot of research has gone into routing and transmission technologies for wireless sensor networks. Conventional routing mechanisms for WSNs still suffer from energy-hole problem caused by difficulties in adaptive route management. Thus, it is imperative that efficient routing mechanisms be developed in order to conserve energy and improve network lifetime. One popular approach is to use meta-heuristic algorithms for optimal path selection in a WSN route management system. A very popular meta-heuristic algorithm used for this objective is the Ant Colony Optimization (ACO) algorithms. ACO has been used as a base for many routing management systems. In this paper an extensive analysis of the performance of ACO based route selection mechanism is reported and also reporting a comparative analysis of efficacy of the ACO routing algorithm over the standard Greedy algorithm in finding routes with different count of sensor nodes and different count of ants. Then find that the ACO routing algorithm outdoes the Greedy algorithm with respect to the number of routes identified.

Improving ant colony optimization algorithm with epsilon greedy and Levy flight

Ant colony optimization (ACO) algorithm is a meta-heuristic and reinforcement learning algorithm, which has been widely applied to solve various optimization problems. The key to improving the performance of ACO is to effectively resolve the exploration/exploitation dilemma. Epsilon greedy is an important and widely applied policy-based exploration method in reinforcement learning and has also been employed to improve ACO algorithms as the pseudo-stochastic mechanism. Levy flight is based on Levy distribution and helps to balance searching space and speed for global optimization. Taking advantage of both epsilon greedy and Levy flight, a greedy–Levy ACO incorporating these two approaches is proposed to solve complicated combinatorial optimization problems. Specifically, it is implemented on the top of max–min ACO to solve the traveling salesman problem (TSP) problems. According to the computational experiments using standard TSPLIB instances, greedy–Levy ACO outperforms max–min ACO and other latest TSP solvers, which demonstrates the effectiveness of the proposed methodology.

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

AbstractMetaheuristic algorithms such as ant colony optimization (ACO) and firefly (FF) have been successfully employed to solve the optimization problems such as robot motion planning in dynamic environments. The systematic plantation of rubber trees on a rectangular grid motivated us to explore application of grid search algorithms. We compared the ACO and FF algorithms in various scenarios by changing simulation parameters like density of the environment, land size, number of robots simultaneously available, and hillock plantations. In all different scenarios, we evaluated the performance of ACO and FF in terms of path length and time of execution, we found that later is outperforming the former. Regression equations are framed to establish the contributions of different parameters. Statistical significance of the results has been in favor of this hypothesis. The shortest path on a plain land is the relatively simplest scenario, while the Hamiltonian on a concave surface is arguably the most difficult. The novelty of this work lies in the very idea of an autonomous robot for the rubber tapping and then path optimization by employing soft computing techniques. This proposal of rubber harvesting robot if implemented for latex collection, has a potential to drive the rubber farming and allied businesses to scale up the economy of the coastal areas of India, say for example, Kerala.

A Novel Ant Colony Optimization Algorithm With Levy Flight

Ant Colony Optimization (ACO) is a widely applied meta-heuristic algorithm. Little researches focused on the candidate selection mechanism, which was developed based on the simple uniform distribution. This paper employs the Levy flight mechanism based on Levy distribution to the candidate selection process and takes advantage of Levy flight that not only guarantees the search speed but also extends the searching space to improve the performance of ACO. Levy ACO incorporating with Levy flight developed on the top of Max-min ACO. According to the computational experiments, the performance of Levy ACO is significantly better than the original Max-min ACO and some latest Traveling Salesman Problem (TSP) solvers.

A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings

Diabetes and cancer are two major life-threatening human chronic disorders that have a high rate of disability and mortality. These diseases have been diagnosed using different deterministic and nature inspired computing algorithms. Here, an effort is made to represent the role of five different insect-based nature inspired computing algorithms [ant colony optimization (ACO), artificial bee colony (ABC), glow-worm swarm optimization (GSO), firefly algorithm (FA) and antlion optimization (ALO)] used for the diagnosis of these two chronic disorders. Initially, the basic statistics of diabetes and cancer patients have been presented. The main intention of this study lies in exploring the usage and performance of ACO, ABC, GSO, FA and ALO in diagnosing different stage and types of diabetes and cancer. It has been revealed that most of the diabetes diagnosis work has been carried out using ACO followed by ABC. As far as cancer is concerned, the three insect-based algorithms, i.e. ACO, ABC and FA, have been also effectively employed for detection of breast, lung, liver, prostate and ovarian cancer. In general, most of the disease diagnostic work has been carried out using ACO, whereas GSO found to be least explored. The rate of predictive accuracy achieved using the hybridization of ACO and neural network is found to be more promising as compared to other individual or hybrid approaches. Likewise, for breast cancer, the amalgamated use of ABC and neural network is more productive. Similarly, the hybrid approach of ACO and neural network is also found useful for early prognosis of lung and gastric cancer. In general, the diagnostic results obtained using hybrid approaches are more promising than their individual use. However, several hybrid combinations are still needed to be explored for the diagnosis of diabetes and different types of cancer, viz. liver, gastric, ovarian, leukaemia as well as a brain tumour. Finally, there is also a scope to use and explore the efficiency of binary and chaotic variants of ACO, ABC, GSO, FA and ALO for the diagnosis of these two critical human disorders.

Differential-Evolution-Based Coevolution Ant Colony Optimization Algorithm for Bayesian Network Structure Learning

Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the performance of ant colony optimization (ACO) in learning the BNs structure, this paper proposes a new improved coevolution ACO (coACO) algorithm, which uses the pheromone information as the cooperative factor and the differential evolution (DE) as the cooperative strategy. Different from the basic ACO, the coACO divides the entire ant colony into various sub-colonies (groups), among which DE operators are adopted to implement the cooperative evolutionary process. Experimental results demonstrate that the proposed coACO outperforms the basic ACO in learning the BN structure in terms of convergence and accuracy.

Solving Vehicle Routing Problem using Ant Colony Optimisation (ACO) Algorithm

Engineering field usually requires having the best design for an optimum performance, thus optimization plays an important part in this field. The vehicle routing problem (VRP) has been an important problem in the field of distribution and logistics since at least the early 1960s. Hence, this study was about the application of ant colony optimization (ACO) algorithm to solve vehicle routing problem (VRP). Firstly, this study constructed the model of the problem to be solved through this research. The study was then focused on the Ant Colony Optimization (ACO). The objective function of the algorithm was studied and applied to VRP. The effectiveness of the algorithm was increased with the minimization of stopping criteria. The control parameters were studied to find the best value for each control parameter. After the control parameters were identified, the evaluation of the performance of ACO on VRP was made. The good performance of the algorithm reflected on the importance of its parameters, which were number of ants (nAnt), alpha (α), beta (β) and rho (ρ). Alpha represents the relative importance of trail, beta represents the importance of visibility and rho represents the parameter governing pheromone decay. The route results of different iterations were compared and analyzed the performance of the algorithm. The best set of control parameters obtained is with 20 ants, α = 1, β = 1 and ρ = 0.05. The average cost and standard deviation from the 20 runtimes with best set of control parameters were also evaluated, with 1057.839 km and 25.913 km respectively. Last but not least, a conclusion is made to summarize the achievement of the study.

Wind Turbine Tower Modeling and Vibration Control Under Different Types of Loads Using Ant Colony Optimized PID Controller

Vibration in the wind turbine tower disturbs the reliability and increases the possibility of structural damage. Design and optimization of vibration controller are a key goal for wind turbine tower to achieve optimal performance. In this study, a proportional integral derivative (PID) is designed and optimized using nature technology to find optimal required force for actuators and therefore, reducing wind turbine tower vibration. PID controller parameters are optimized with ant colony optimization (ACO) and compared with traditional tuning methods such as Ziegler–Nichols and Tyreus–Luyben methods to ensure its effectiveness in minimizing wind turbine tower vibration. The optimized active vibration controller shows better performance than traditional method in terms of vibration reduction rate, ability to adapt when frequency varies and computational time. This paper also investigated finite difference method for wind turbine tower modeling, and its efficacy is compared with another well-known numerical finite element method based on mean squared error, fit to estimated data and cross signature assurance criterion. The performance of ACO optimized PID controller is investigated for wind turbine tower under four different types of disturbances and compared with uncontrolled and passive controlled system. Results show that 98, 84, 92 and 98% of displacement of the tower are reduced under simulated blade/rotor imbalance, impact, wind and turbulence disturbances, respectively, using ACO optimized PID controller.

Performance Analysis of ACO on the Quadratic Assignment Problem

The Quadratic assignment problem (QAP) is to assign a set of facilities to a set of locations with given distances between the locations and given flows between the facilities such that the sum of the products between flows and distances is minimized, which is a notoriously difficult NP-hard combinatorial optimization problem. A lot of heuristics have been proposed for the QAP problem, and some of them have proved to be efficient approximation algorithms for this problem. Ant colony optimization (ACO) is a general-purpose heuristic and usually considered as an approximation algorithms for NP-hard optimization problems. However, we know little about the performance of ACO for QAP from a theoretical perspective. This paper contributes to a theoretical understanding of ACO on the QAP problem. The worst-case bound on a simple ACO algorithm called (1+1) Max-min ant algorithm ((1+1) MMAA) for the QAP problem is presented. It is shown that a degenerate (1+1) MMAA finds an approximate solution on the QAP problem. Finally, we reveal that ACO can outperform the 2-exchange local search algorithm on an instance of the QAP problem.

A joint internal production and external supplier order lot size optimization under defective manufacturing and rework

The traditional production-inventory problems do not consider joint production and order lot sizes in manufacturing systems. This paper considers a defective system to jointly decide on order and production lot sizes in a manufacturing company. The manufacturer produces items with finite processing rate where a percentage of these items are of imperfect quality. At the end of production process, the rework of imperfect items is carried out. The demand rate and the number of defective items are considered to be uncertain parameters. The aim is to determine optimal number of sub-production cycles, as well as optimal order quantity. To address this problem, we first define the problem and describe its characteristics, build the model via deriving total cost function, design an appropriate solution algorithm, and then illustrate and evaluate the results. The solution algorithm is based on an ant colony optimization (ACO) which is enhanced by an adaptive learning strategy. A numerical example is presented and discussed, and the results are compared with basic ACO and genetic algorithm (GA). Finally, sensitivity analysis and robustness analysis are performed and discussed. The observed results reveal the robust performance of proposed approach against basic ACO and GA. Moreover, we observed that the performance of basic ACO and GA is approximately the same, and the basic ACO is more robust than GA in our experiments.