Ant Colony Optimization Approach Research Articles

Design, analysis and optimization of a composite leaf spring for light vehicles

Leaf spring is the most widely used suspension system in automobiles which is subjected to various types of stresses and deflection. Generally, steel is used as a material for the leaf spring but steel is having high density due to which its weight is also more. There is a lot of scopes to replace the steel with other material to reduce the total weight of the vehicle. The advancement in plastic-reinforced fibre is useful for decreasing the weight of the component the composite materials are having high strength and low weight as compared to steel. In this proposed work composite material is used in place of conventional steel. The composite material is made up of glass and fibre called Epoxy resin. Various test is performed to carry out the properties of the composite material. Ant colony optimization approach is used to design and optimization of composite leaf springs. A geometric model of the leaf spring is prepared in the CATIA V5 software and analysis is done. It is found that composite springs can sustain the load the same as that of the steel leaf spring, and less deflection is observed in composite springs than in steel leaf springs. The density of the composite material is low so eventually, weight is also reduced by 70 % to 80%.

Yin-yang framework of decision-making: Development and assessment of Yin-yang decision-making styles questionnaire

Decision-making styles represent the typical manner individuals use to make decisions. This study aimed to develop and test the structure of a decision-making style questionnaire based on the Yin-yang framework of decision making. Two studies were carried out. In Study 1, we generated an initial item pool of 122 items using a theory-driven approach. Then three focus groups were used to finalize 54 items for the field testing. Third, using a hybrid of iterative exploratory factor analysis and ant colony optimization approach (n = 895), we identified 23 items that were deemed best to represent five constructs of the yin-yang framework of decision making: adaptability, relative positioning, self-awareness, proactivity, and prudence. In Study 2, we performed the validation study (n = 569) by testing three models of the yin-yang framework of decision making styles. We also explored age and sex differences. The results from two studies provide initial support for the factor structure and reliability of the Yin-yang decision-making questionnaires. The results were discussed in the context of the yin-yang framework and age and sex differences regarding these constructs.

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.

Performance optimization of 5G NR technology for industrial automation

Communication over the wireless has significantly evolved to the level where it is capable of supporting the essential controlling mechanisms of the high-definition automated industrial process. The Fifth-Generation (5G) mobile networking air-based interface, named New Radio (5GNR), is presently capable of adopting scalable control and subband data transmission for small data packets, which has made it efficiently compatible for the critical controlling application of industrial automation processes. The minimum cycle time is used as a reliable parameter for an indication of the healthy techniques for industrial wireless telecommunication, but it has not undergone deep investigation within the framework of modern 5G technology. For addressing such types of challenges, this paper has taken into consideration 5G-based industrial automation process control through wireless networking by using a delay optimization approach associated with data-base channel control using the Ant Colony Optimization (ACO) approach for solving the proposed evaluation of a reliable minimum cycle time over 5G network communication.

Ant colony optimization technique optimized controller for frequency stabilization of an isolated power system with non-linearity

The load frequency control of a thermal power producing system, the ant colony optimization (ACO) approach adjusted proportional integral derivative (PID) controller is proposed. This work examines the single area thermal power system with /without generation rate constraint. For emergency situations, a PID controller is developed and used to regulate power system characteristics. This study uses the ACO method with the integral time absolute error objective function optimize controller gain values. In addition, the performance of the suggested approach is evaluated by introducing non-linearity components to the power-generating systems under study. Conventional, particle swarm optimization (PSO), and genetic algorithms (GA) approach tuned controller responses are compared with ACO. Fast settling with minimal peak and undershoots in the producing power supply of the system under emergency loading situations demonstrates the superiority of the proposed controller over competing controllers.

A novel ant colony optimisation approach for fretting out wormhole attack in mobile ad hoc networks

A novel ant colony optimisation approach for fretting out wormhole attack in mobile ad hoc networks

A novel ant colony optimisation approach for fretting out wormhole attack in mobile ad hoc networks

A novel ant colony optimisation approach for fretting out wormhole attack in mobile ad hoc networks

AntNetAlign: Ant Colony Optimization for Network Alignment

Network Alignment (NA) is a hard optimization problem with important applications such as, for example, the identification of orthologous relationships between different proteins and of phylogenetic relationships between species. Given two (or more) networks, the goal is to find an alignment between them, that is, a mapping between their respective nodes such that the topological and functional structure is well preserved. Although the problem has received great interest in recent years, there is still a need to unify the different trends that have emerged from diverse research areas. In this paper, we introduce AntNetAlign, an Ant Colony Optimization (ACO) approach for solving the problem. The proposed approach makes use of similarity information extracted from the input networks to guide the construction process. Combined with an improvement measure that depends on the current construction state, it is able to optimize any of the three main topological quality measures. We provide an extensive experimental evaluation using real-world instances that range from Protein–Protein Interaction (PPI) networks to Social Networks. Results show that our method outperforms other state-of-the-art approaches in two out of three of the tested scores within a reasonable amount of time, specially in the important S3 score. Moreover, it is able to obtain near-optimal results when aligning networks with themselves. Furthermore, in larger instances, our algorithm was still able to compete with the best performing method in this regard.

Memory-Based Ant Colony System Approach for Multi-Source Data Associated Dynamic Electric Vehicle Dispatch Optimization

The developments of electric vehicle (EV) technology and mobile internet technology have made the EV-oriented ride-hailing service a trend in smart cities. In the service scenario, a high-quality order allocation approach is in great need to quickly process a series of customer request orders, so as to reduce total customer waiting time and transportation cost. To simulate real-world customer-EV allocation scenarios, in this paper, a dynamic EV dispatch (DEVD) model is established by considering multi-source data association from five sources, including customer, vehicle, charging, station, and service. To solve the proposed multi-source data associated DEVD model, a memory-based ant colony optimization (MACO) approach is developed. MACO maintains a memory archive to store the historically good solutions, which not only can be used to update pheromone to guide the search, but also can be used to help the reactions to environmental changes. In response to dynamic changes, a partial reassignment strategy is also proposed to re-optimize some of the assigned customer-EV pairs in the historically best solution. Moreover, an exchange or replace local search procedure is designed to enhance the performance. The MACO algorithm is applied to a set of dynamic test cases with different customer request and EV sizes. Experimental results show that MACO generally outperforms the first-come-first-served approach and some state-of-the-art ACO-based dynamic optimization algorithms.

An ant colony optimization approach to the multi-vehicle prize-collecting arc routing for connectivity problem

Blocked roads can jeopardize emergency response activities in the aftermath of disasters, such as earthquakes. In many cases, the distribution of relief supplies to affected communities, the evacuation of victims, and Search and Rescue activities cannot be resumed due to the lack of connectivity of the road network. The multi-vehicle prize collecting arc routing for connectivity problem (KPC-ARCP) aims to determine the routes of synchronized road clearing teams during the immediate response of a disaster, to reconnect the network so that the utility of the reconnection is maximized. This paper proposes an Ant Colony Optimization (ACO) algorithm to solve the KPC-ARCP and compares its performance to results of earlier studies which apply GRASP and Matheuristic methods to solve the problem. Performance comparisons consider the computation time and accuracy of the solution, and are implemented on academic and real cases in Istanbul. The runs on academic and real-world instances indicate that ACO has a reasonable performance compared to the existing methods. However, the high complexity of its parameter tuning suggests that GRASP is likely more suitable for KPC-ARCP.

Negative Learning Ant Colony Optimization for MaxSAT

Recently, a new negative learning variant of ant colony optimization (ACO) has been used to successfully tackle a range of combinatorial optimization problems. For providing stronger evidence of the general applicability of negative learning ACO, we investigate how it can be adapted to solve the Maximum Satisfiability problem (MaxSAT). The structure of MaxSAT is different from the problems considered to date and there exists only a few ACO approaches for MaxSAT. In this paper, we describe three negative learning ACO variants. They differ in the way in which sub-instances are solved at each algorithm iteration to provide negative feedback to the main ACO algorithm. In addition to using IBM ILOG CPLEX, two of these variants use existing MaxSAT solvers for this purpose. The experimental results show that the proposed negative learning ACO variants significantly outperform the baseline ACO as well as IBM ILOG CPLEX and the two MaxSAT solvers. This result is of special interest because it shows that negative learning ACO can be used to improve over the results of existing solvers by internally using them to solve smaller sub-instances.

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system (AIS) and ant colony optimization (ACO) approaches are utilized to cultivate RBFnet for function approximation issue. The proposed hybridization of AIS and ACO approaches optimization (HIAO) algorithm combines the complementarity of exploitation and exploration to realize problem solving. It allows the solution domain having the advantages of intensification and diversification, which further avoids the situation of immature convergence. In addition, the empirical achievements have confirmed that the HIAO algorithm not only obtained the best accurate function approximation for theoretically standard nonlinear problems, it can be further applied on the instance solving for practical crude oil spot price prediction.

Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach.

Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise during a collective flight, path planning might get complicated. The study needs to employ hybrid algorithms of bio-inspired computations to address path planning issues with more stability and speed. In this article, two hybrid models of Ant Colony Optimization were compared with respect to convergence time, i.e., the Max-Min Ant Colony Optimization approach in conjunction with the Differential Evolution and Cauchy mutation operators. Each algorithm was run on a UAV and traveled a predetermined path to evaluate its approach. In terms of the route taken and convergence time, the simulation results suggest that the MMACO-DE technique outperforms the MMACO-CM approach.

Ergonomic Risk Minimization in the Portuguese Wine Industry: A Task Scheduling Optimization Method Based on the Ant Colony Optimization Algorithm

In the wine industry, task planning is based on decision-making processes that are influenced by technical and organizational constraints as well as regulatory limitations. A characteristic constraint inherent to this sector concerns occupational risks, in which companies must reduce and mitigate work-related accidents, resulting in lower operating costs and a gain in human, financial, and material efficiency. This work proposes a task scheduling optimization model using a methodology based on the ant colony optimization approach to mitigate the ergonomic risks identified in general winery production processes by estimating the metabolic energy expenditure during the execution of tasks. The results show that the tasks were reorganized according to their degree of ergonomic risk, preserving an acceptable priority sequence of tasks with operational affinity and satisfactory efficiency from the point of view of the operationalization of processes, while the potential ergonomic risks are simultaneously minimized by the rotation and alternation of operative teams between those tasks with higher and lower values of metabolic energy required. We also verified that tasks with lower ergonomic-load requirements influence the reorganization of the task sequence by lowering the overall value of metabolic energy, which is reflected in the reduction of the ergonomic load.

A Fuzzy Logic for Parameter Adaptation in Ant Colony Optimization Approach

A Fuzzy Logic for Parameter Adaptation in Ant Colony Optimization Approach

Towards delay-optimized and resource-efficient network function dynamic deployment for VNF service chaining

By decoupling virtualized network functions from the dedicated network equipment on which they run, Network Function Virtualization (NFV) has brought a flexible and economical way to support the complex communication demands of different applications. Virtualized Network Functions (VNFs) can be dispatched and deployed as instances of plain software on or near the communication paths of applications to establish Service Function Chains (SFCs), so as to provide special packet processing operations beyond simple packet forwarding. However, it is still a great challenge to dynamically place appropriate network functions at suitable locations so as to improve the efficiency of establishing SFCs and optimize network resource utilization. In this paper, the mechanism of dynamically deploying customized network functions via NFV is proposed. By predicting the future popularities of applications to switches, it adaptively places most of the appropriate network functions in corresponding forwarding equipment before they are massively requested. The serious latency and extra resource consumption caused by real-timely dispatching and deploying most of the requested network functions will be avoided. Then, the approach of Ant Colony Optimization (ACO) inspired multi-switch cooperative network function providing is devised. By cooperating multiple forwarding equipment on the packet transmission path, it makes full use of the already placed network functions to support packet processing operations in time with the cost and delay factors jointly considered. Simulation results show that the proposed mechanism has significant improvements in time overhead and resource utilization compared with the current state of the art. Specifically, our mechanism is capable of improving the service delay, the function utilization ratio, and the SFC adjustment efficiency by about 14%, 10% and 12% respectively, compared with corresponding work.

Surrogate-assisted population based ACO for resource constrained job scheduling with uncertainty

In mining supply chains, transporting ore from mines to ports, is a problem of significant interest. Resources required in transporting ore are limited, and hence using resource efficiently goes a long way towards solving this problem. A key issue is that the resource availability is subject to uncertainties. Previous studies have considered similar (deterministic) problems, commonly tackled as resource constrained job scheduling. However, the uncertainty associated with resources needed to transport ore has not been considered. This study extends previous ones, by investigating resource constrained job scheduling with uncertainty (considering resource availabilities). The focus is on robust optimisation, where the aim is to find high-quality solutions irrespective of the input data. To solve this problem a population-based ant colony optimisation approach is devised, with the aim of identifying high-quality solutions across several uncertain scenarios. Moreover, computing the objective is inefficient due to uncertainty, and hence surrogate models are used. Experiments are conducted on a wide range of problem instances with varying uncertainty levels considering resources, and find that population-based ant colony optimisation is superior to ant colony optimisation on its own. The key advantage of this method is that it is able to maintain a population of excellent solutions, which can be used to efficiently approximate the objective values of new solutions, and hence update the pheromones globally of the ant colony optimisation algorithm. Using this information the surrogate model considers only high quality solutions, and by carrying out increased learning in short time-frames, achieves excellent results.

AN EFFICIENT GRAPH THEORY-BASED ALGORITHM FOR SHIP TRAJECTORY PLANNING

The research presented in this paper is dedicated to the development of a path planning algorithm for a moving object in a dynamic environment. The marine environment constitutes the application area. A graph theory-based path planning method for ships is introduced and supported by the results of simulation tests and comparative analysis with a heuristic Ant Colony Optimization approach. The method defines the environment with the use of a visibility graph and uses the A* algorithm to find the shortest, collision-free path. The main contribution is the development of an effective graph theory-based algorithm for path planning in an environment with static and dynamic obstacles. The computational time does not exceed a few seconds. Obtained results allow to state that the method is suitable for use in an intelligent motion control system for ships.

A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization

<span>This study proposed a hybrid approach of firefly algorithm (FA) and ant colony optimization (ACO) for solving multi-depot multiple traveling salesman problem, a TSP with more than one salesman and departure city. The FA is fast converging but easily trapped into the local optimum. The ACO has a great ability to search for the solution but it converges slowly. To get a better result and convergence time, we integrate FA to find the local solutions and ACO to find a global solution. The local solutions of the FA are normalized then initialized to the quantity of pheromones for running the ACO. Furthermore, we experimented with the best parameters in order to optimize the solution. In justification, we used the sea transportation route in Indonesia as a case study. The experimental results showed that the hybrid approach of FA and ACO has superior performance with an average computational time of 26.90% and converges 32.75% faster than ACO.</span>

Efficiently solving the thief orienteering problem with a max–min ant colony optimization approach

We tackle the thief orienteering problem (ThOP), an academic multi-component problem that combines two classical combinatorial problems, namely the Knapsack Problem and the Orienteering Problem. In the ThOP, a thief has a time limit to steal items that distributed in a given set of cities. While traveling, the thief collects items by storing them in their knapsack, which in turn reduces the travel speed. The thief has as the objective to maximize the total profit of the stolen items. In this article, we present an approach that combines swarm-intelligence with a randomized packing heuristic. Our solution approach outperforms existing works on almost all the 432 benchmarking instances, with significant improvements.