Showing papers on "Ant colony published in 2020"

Research on path planning of mobile robot based on improved ant colony algorithm

Abstract: To solve the problems of local optimum, slow convergence speed and low search efficiency in ant colony algorithm, an improved ant colony optimization algorithm is proposed. The unequal allocation initial pheromone is constructed to avoid the blindness search at early planning. A pseudo-random state transition rule is used to select path, the state transition probability is calculated according to the current optimal solution and the number of iterations, and the proportion of determined or random selections is adjusted adaptively. The optimal solution and the worst solution are introduced to improve the global pheromone updating method. Dynamic punishment method is introduced to solve the problem of deadlock. Compared with other ant colony algorithms in different robot mobile simulation environments, the results showed that the global optimal search ability and the convergence speed have been improved greatly and the number of lost ants is less than one-third of others. It is verified the effectiveness and superiority of the improved ant colony algorithm.

An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application

Abstract: In this paper, an effective improved co-evolution ant colony optimisation (MSICEAO) algorithm is presented to solve complex optimisation problem. In the MSICEAO, the multi-population co-evolution s...

A new efficient hybrid algorithm for large scale multiple traveling salesman problems

Abstract: Multiple traveling salesmen problem (MTSP) is not only a generalization of the traveling salesman problem (TSP), but also more suitable for modeling practical problems in the real life than TSP. For solving the MTSP with multiple depots, the requirement of minimum and maximum number of cities that each salesman should visit, a hybrid algorithm called ant colony-partheno genetic algorithms (AC-PGA) is provided by combining partheno genetic algorithms (PGA) and ant colony algorithms (ACO). The main idea in this paper is to divide the variables into two parts. In detail, it exploits PGA to comprehensively search the best value of the first part variables and then utilizes ACO to accurately determine the second part variables value. For comparative analysis, PGA, improved PGA (IPGA), two-part wolf pack search (TWPS), artificial bee colony (ABC) and invasive weed optimization (IWO) algorithms are adopted to solve MTSP and validated with publicly available TSPLIB benchmarks. The results of comparative experiments show that AC-PGA is sufficiently effective in solving large scale MTSP and has better performance than the existing algorithms.

Energy-efficient scheduling for multi-objective two-stage flow shop using a hybrid ant colony optimisation algorithm

Abstract: Reducing energy costs has become an important concern for sustainable manufacturing systems, owing to concern for the environment. We present a multi-objective hybrid ant colony optimisation (MHACO...

MPPT in PV systems using ant colony optimisation with dwindling population

Abstract: Ant colony optimisation has been tailored to suit maximum power point tracking (MPPT) in photovoltaic (PV) systems and is presented in this study. Artificial ants are deployed in the solution space and are made to forage and the ants which find better sources of food are retained while ants fail to search effectively are deleted from the population. The greedy search of potential ants for better food location leads to identification of higher power peaks in the PV system. The concept is modelled suitably and MPPT curves in a few PV configurations are simulated and found to be promising. Experiments were also conducted to show the veracity of the new method.

Robust Scheduling of Hot Rolling Production by Local Search Enhanced Ant Colony Optimization Algorithm

Abstract: Scheduling of a hot strip mill is an important decision problem in the steel manufacturing industry. Previous studies on the hot strip mill scheduling problem have mostly neglected the random factors in production. However, random variations in processing times are inevitable due to unpredictable delays and disturbances. In this article, we adopt a robust optimization approach to deal with the uncertainty in processing times. The advantage is that no assumption has to be made regarding the distribution of random data, and the obtained schedule will remain strictly feasible when the variations have not exceeded a predefined uncertainty set. First, a mixed-integer linear programming model is presented to formulate the robust scheduling problem. Then, a hybrid metaheuristic algorithm, which combines ant colony system (ACS) and enhanced local search, is proposed to provide an efficient solution to the problem. Finally, extensive computational experiments involving both randomly generated and real-world instances have been conducted to verify the effectiveness of the proposed algorithm. It is shown that the algorithm achieves optimality for small instances and outperforms two state-of-the-art metaheuristics when used to solve large instances.

Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization

Abstract: Optimal performance and improved lifetime are the most desirable design benchmarks for WSNs and the mechanism for data gathering is a major constituent influencing these standards. Several researchers have provided significant evidence on the advantage of mobile sink (MS) in performing effective gathering of relevant data. However, determining the trajectory for MS is an NP-hard-problem. Especially in delay-inevitable applications, it is challenging to select the best-stops or rendezvous points (RPs) for MS and also to design an efficient route for MS to gather data. To provide a suitable solution to these challenges, we propose in this paper, a game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique. This is a distributed method of data gathering using MS, combining the optimal decision making skill of game theory in selecting the best RPs and computational swarm intelligence of enhanced ant colony optimization in choosing the best path for MS. GTAC-DG helps to reduce data transfer and management, energy consumption and delay in data delivery. The MS moves in a reliable and intelligent trajectory, extending the lifetime and conserving the energy of WSN. The simulation results prove the effectiveness of GTAC-DG in terms of metrics such as energy and network lifetime.

Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning

Abstract: This chapter starts with the inspiration and main mechanisms of one of the most well-regarded combinatorial optimization algorithms called Ant Colony Optimizer (ACO). This algorithm is then employed to find the optimal path for an AUV. In fact, the problem investigated is a real-world application of the Traveling Salesman Problem (TSP).

A novel ant colony optimization based on game for traveling salesman problem

Abstract: Ant Colony Optimization (ACO) algorithms tend to fall into local optimal and have insufficient astringency when applied to solve Traveling Salesman Problem (TSP). To address this issue, a novel game-based ACO (NACO) is proposed in this report. NACO consists of two ACOs: Ant Colony System (ACS) and Max-Min Ant System (MMAS). First, an entropy-weighted learning strategy is proposed. By improving diversity adaptively, the optimal solution precision can be optimized. Then, to improve the astringency, a nucleolus game strategy is set for ACS colonies. ACS colonies under cooperation share pheromone distribution and distribute cooperative profits through nucleolus. Finally, to jump out of the local optimum, mean filtering is introduced to process the pheromone distribution when the algorithm stalls. From the experimental results, it is demonstrated that NACO has well performance in terms of both the solution precision and the astringency.

Cold Chain Logistics Path Optimization via Improved Multi-Objective Ant Colony Algorithm

Abstract: In order to improve the performance and change the current situation of the cost minimization model widely used in the cold chain logistics distribution process, a multi-objective optimization model based on cost, carbon emissions and customer satisfaction is proposed. Considering the characteristic of this proposed optimization model, we design an improved ant colony algorithm with a multi-objective heuristic function to solve it, termed as ACOMO. Experimental results show that the proposed ACOMO can effectively solve the vehicle routing problem of the multi-objective optimization model, and outperforms the classic ant colony algorithms, resulting in more Pareto optimal solutions. It offers an environmentally friendly distribution solution for the problem. Specifically, the distribution path obtained by the improved ant colony algorithm manages to achieve the above multiple goals, including reduction of distribution costs and carbon emissions, and improvement of customer satisfaction. In addition, compared with a single-target model that only provides one single distribution route to cost minimization, multi-objective optimization can provide a variety of distribution route options for logistics companies in practice. Finally, through the sensitivity analysis of temperature changes and cargo damage coefficients, the proposed system successfully provides reference for the optimization of the path of cold chain logistics enterprises, and promotes logistics enterprises to effectively arrange their work and to be more socially responsible.

ANFIS grid partition framework with difference between two sigmoidal membership functions structure for validation of nanofluid flow.

Abstract: In this study, a square cavity is modeled using Computational Fluid Dynamics (CFD) as well as artificial intelligence (AI) approach. In the square cavity, copper (Cu) nanoparticle is the nanofluid and the flow velocity characteristics in the x-direction and y-direction, and the fluid temperature inside the cavity at different times are considered as CFD outputs. CFD outputs have been assessed using one of the artificial intelligence algorithms, such as a combination of neural network and fuzzy logic (ANFIS). As in the ANFIS method, we have a non-dimension procedure in the learning step, and there is no issue in combining other characteristics of the flow and thermal distribution beside the x and y coordinates, we combine two coordinate parameters and one flow parameter. This ability of method can be considered as a meshless learning step that there is no instability of the numerical method or limitation of boundary conditions. The data were classified using the grid partition method and the MF (membership function) type was dsigmf (difference between two sigmoidal membership functions). By achieving the appropriate intelligence in the ANFIS method, output prediction was performed at the points of cavity which were not included in the learning process and were compared to the existing data (the results of the CFD method) and were validated by them. This new combination of CFD and the ANFIS method enables us to learn flow and temperature distribution throughout the domain thoroughly, and eventually predict the flow characteristics in short computational time. The results from AI in the ANFIS method were compared to the ant colony and fuzzy logic methods. The data from CFD results were inserted into the ant colony system for the training process, and we predicted the data in the fuzzy logic system. Then, we compare the data with the ANFIS method. The results indicate that the ANFIS method has a high potentiality compared to the ant colony method because the amount of R in the ANIFS system is higher than R in the ant colony method. In the ANFIS method, R is equal to 0.99, and in the ant colony method, R is equal to 0.91. This shows that the ant colony needs more time for both the prediction and training of the system. Also, comparing the pattern recognition in the two systems, we can obviously see that by using the ANFIS method, the predictions completely match the target points. But the other method cannot match the flow pattern and velocity distribution with the CFD method.

Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms

Abstract: Estimation of distribution algorithms (EDAs) are a successful branch of evolutionary algorithms (EAs) that evolve a probabilistic model instead of a population. Analogous to genetic drift in EAs, EDAs also encounter the phenomenon that the random sampling in the model update can move the sampling frequencies to boundary values not justified by the fitness. This can result in a considerable performance loss. This article gives the first tight quantification of this effect for three EDAs and one ant colony optimizer, namely, for the univariate marginal distribution algorithm, the compact genetic algorithm, population-based incremental learning, and the max–min ant system with iteration-best update. Our results allow to choose the parameters of these algorithms in such a way that within a desired runtime, no sampling frequency approaches the boundary values without a clear indication from the objective function.

Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization

Abstract: This paper presents the first results of an agent-based model aimed at solving a Capacitated Vehicle Routing Problem (CVRP) for inbound logistics using a novel Ant Colony Optimization (ACO) algorithm, developed and implemented in the NetLogo multi-agent modelling environment. The proposed methodology has been applied to the case study of a freight transport and logistics company in South Italy in order to find an optimal set of routes able to transport palletized fruit and vegetables from different farms to the main depot, while minimizing the total distance travelled by trucks. Different scenarios have been analysed and compared with real data provided by the company, by using a set of key performance indicators including the load factor and the number of vehicles used. First results highlight the validity of the method to reduce cost and scheduling and provide useful suggestions for large-size operations of a freight transport service.

Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions

Abstract: A Multi-Depot Green Vehicle Routing Problem (MDGVRP) is considered in this paper. In MDGVRP, Alternative Fuel-powered Vehicles (AFVs) start from different depots, serve customers, and, at the end, return to the original depots. The limited fuel tank capacity of AFVs forces them to visit Alternative Fuel Stations (AFS) for refueling. The objective is to minimize the total carbon emissions. A Two-stage Ant Colony System (TSACS) is proposed to find a feasible and acceptable solution for this NP-hard (Non-deterministic polynomial-time) optimization problem. The distinct characteristic of the proposed TSACS is the use of two distinct types of ants for two different purposes. The first type of ant is used to assign customers to depots, while the second type of ant is used to find the routes. The solution for the MDGVRP is useful and beneficial for companies that employ AFVs to deal with the various inconveniences brought by the limited number of AFSs. The numerical experiments confirm the effectiveness of the proposed algorithms in this research.

An efficient improvement of ant colony system algorithm for handling capacity vehicle routing problem

Abstract: Article history: Received November 1

An Improved Ant Colony Algorithm for Optimized Band Selection of Hyperspectral Remotely Sensed Imagery

Abstract: The ant colony algorithm (ACA) has been widely used for reducing the dimensionality of hyperspectral remote sensing imagery. However, the ACA suffers from problems of slow convergence and of local optima (caused by loss of population diversity). This paper proposes an improved ant colony algorithm (IMACA) based band selection algorithm (IMACA-BS), to overcome the two shortcomings of the standard ACA. For the former problem, a pre-filter is applied to improve the heuristic desirability of the ant colony system; the Pearson’s similarity measurement of the degree of redundancy among the selected bands is taken as one of the terms in the heuristic function, and this further accelerates the convergence of the IMACA-BS. For the latter problem, a pseudo-random rule and an adaptive information update strategy are, respectively, introduced to increase the population diversity of the ant colony system. The effectiveness of the proposed algorithm was evaluated on three public datasets (Indian Pines, Pavia University and Botswana datasets), and compared with a series of benchmarks. Experimental results demonstrated that the IMACA-BS consistently achieved the highest overall classification accuracies and significantly outperformed other benchmarks over all of the three experiments. The proposed IMACA-BS is, therefore, recommended as an effective alternative for band selection of hyperspectral imagery.

BIO-INSPIRED COMMUNICATION: A Review on Solution of Complex Problems for Highly Configurable Systems

Abstract: Data science-based problems are becoming a challenge due to explosive amount of data, if not dreadful. To solve versatile complex problems for Highly configurable systems intelligent algorithms are in consideration. However, the awareness of the crossed strategy in researchers is gradually decreasing due to rapid growth of the field, so that the literature of bio inspired communication is only inclined to a few problem-solving algorithms (such as neural networks, ant colonies optimization, genetic algorithms, and particle swarms). In this paper we specifically elaborate the working of neural networks such as genetic algorithms like Ant Colony Optimization (ACO), Artificial bee colony (ABC), Fire Flies, Bacterial Search, Particle Swarm Optimization (PSO), Rhododendron Search, and other genetic algorithms like BAT, and Frog. This revision will pave the way for future research to select an algorithm based on adjustment. Communication in swarms has 3 driven rules. The attributes of the groups of swarms are emergent and stigmatic. Different insects, such as ants, wasps, termites, perform local work for a global purpose with ample resilience as they are not centrally shielded. Finally, the authors discuss biologically inspired communication usage in applications.

Multi-objective optimization of auto-body fixture layout based on an ant colony algorithm:

Abstract: Fixtures are extensively used in many industries such as the car industry, to locate and constrain the sheet part during the assembly stage. Fixture layout affects on deformation of sheet parts. Th...

Energy-aware dynamic-link load balancing method for a software-defined network using a multi-objective artificial bee colony algorithm and genetic operators

Abstract: Information and communication technology (ICT) is one of the sectors that have the highest energy consumption worldwide. It implies that the use of energy in the ICT must be controlled. A software-defined network (SDN) is a new technology in computer networking. It separates the control and data planes to make networks more programmable and flexible. To obtain maximum scalability and robustness, load balancing is essential. The SDN controller has full knowledge of the network. It can perform load balancing efficiently. Link congestion causes some problems such as long transmission delay and increased queueing time. To overcome this obstacle, the link load balancing strategy is useful. The link load-balancing problem has the nature of NP-complete; therefore, it can be solved using a meta-heuristic approach. In this study, a novel energy-aware dynamic routing method is proposed to solve the link load-balancing problem while reducing power consumption using the multi-objective artificial bee colony algorithm and genetic operators. The simulation results have shown that the proposed scheme has improved packet loss rate, round trip time and jitter metrics compared with the basic ant colony, genetic-ant colony optimisation, and round-robin methods. Moreover, it has reduced energy consumption.

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

Abstract: 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.