Version Of Ant Colony Optimization Research Articles

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation.

The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur's entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.

Efficient Vehicle Routing Solutions: A Metaheuristic Approach

Efficient vehicle directing is fundamental for smoothing out strategies processes, cutting costs, and raising consumer satisfaction. The current study explores the usage of metaheuristic procedures to handle multifaceted vehicle directing issues that emerge in various fields, including public transportation frameworks, transportation logistics, and delivery services. The study is explicitly worried about the application and investigation of a modified version of Ant Colony Optimization (ACO), a metaheuristic algorithm that draws motivation from ant foraging behavior. To increment solution quality, robustness, and convergence speed, the modified ACO algorithm incorporates enhancements like multi-colony systems, local search heuristics, and dynamic pheromone update instruments. This examination shows the adequacy and adaptability of the recommended approach in delivering ideal or almost ideal routing arrangements through mathematical modeling, theoretical analysis, and empirical assessments utilizing real-world datasets. The outcomes add to the corpus of information on optimization algorithms and give valuable exhortation to working on the manageability and proficiency of vehicle routing operations.

A multi-objective optimization strategy for timber forwarding in cut-to-length harvesting operations

ABSTRACT Optimization of forwarding work on the trail network created by the harvester in cut-to-length operations carries much potential for increasing the efficiency and reducing the environmental impact of timber removal. While several approaches to this optimization task have been proposed, existing methods focus on optimizing a single objective at a time. In this work, a heuristic multi-objective optimization strategy for the problem is presented, where the route length, operation duration, and soil damage can be optimized simultaneously. The strategy is based on a tailored version of ant colony optimization and linear scalarization of the full multi-objective problem. The relative importance of the objectives can be set via tunable weights, and the approach is of a modular nature. Results on a realistically sized clearcut harvest site indicate that the strategy produces promising results and gives the expected qualitative behavior when optimizing either a single objective or two objectives at a time.

An intelligent driven deep residual learning framework for brain tumor classification using MRI images

Brain tumor classification is an expensive complicated challenge in the sector of clinical image analysis. Machine learning algorithms enabled radiologists to accurately diagnose tumors without requiring major surgery. However, several challenges rise; first, the major challenge in designing the most accurate deep learning architecture for classifying brain tumors; and secondly, difficulty of finding an expert who is experienced in the field of classifying brain tumors using images by deep learning models. These difficulties made us motivated to propose an advanced and high accurate framework based on the concepts of deep learning and evolutionary algorithms to automatically design the ResNet architecture efficiently for classifying three types of brain tumors on a large database of MRI images. Thus, we propose an optimization-based deep convolutional ResNet model combined with a novel evolutionary algorithm to optimize the architecture and hyperparameters of deep ResNet model automatically without need of human experts as well manual architecture design which is complicated task to classify different types of brain tumor. Also, we propose an improved version of ant colony optimization (IACO) based on the concepts of differential evolution strategy and multi-population operators. These two concepts make an effective balance for solution diversity and convergence speed as well as enhancing the optimization performance and avoiding falling into the local optima for designing the deep learning-based ResNet architectures. The experimental finding revealed that our proposed framework obtained an average accuracy of 0.98694 which efficiently shows that our IACO-ResNet algorithm can help excellently with the automatic classification of brain tumors.

A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation

In this paper, a Mixed-Integer Linear Programming (MILP) model to simultaneously schedule jobs and transporters in a flexible flow shop system is suggested. Wherein multiple jobs, finite transporters, and stages with parallel unrelated machines are considered. In addition to the mentioned technicalities, the jobs are able to omit one or more stages, and may not be executable by all the machines, and similarly, transportable by all the transporters. To the best of our knowledge, no study in the literature has featured efficacy of the parallel computing in simultaneous scheduling of jobs and transporters in the flexible flow shop system which remarkably shortens run time if the solution approaches are designed accordingly. To this end, we employ Gurobi solver, Parallel Genetic Algorithm (PGA), Parallel Particle Swarm Optimization (PPSO) and hybrid Parallel PSO-GA Algorithm (PPSOGA) to deal with the problem instances. Furthermore, a parallel version of Ant Colony Optimization (ACO) algorithm adapted from the state-of-the-art literature is developed to verify the performance of our suggested solution methods. Using 60 problem instances generated via uniform distribution, the suggested solution approaches are compared against one another. After assessing the results of the computational experiments, it is deduced that PPSOGA algorithm outperforms PGA, PPSO, Parallel Ant Colony Optimization (PACO) and Gurobi solver in terms of the quality of the solutions. The efficiency and run time of the suggested approaches are then assessed through two prominent statistical tests (i.e., Wald and Analysis of Variance (ANOVA)). Eventually, it comes to spotlight that PPSOGA algorithm is computationally rewarding and dependable.

Enhancement of motor speed identification using artificial neural networks

In this study have been utilized a modified version of ant colony optimization to improve the thresholds of neural networks and weights by including therank-weight approach. Furthermore, this technique easily overcome the drawbacks speed up convergence into the minimum while training the backpropagation neural network. The improved ant colony optimization-backpropagation neural.not only has the capacity to map extensively, but it also enhances operating efficiency noticeably, according to the simulation findings. The simulation results revealed that the speed sensor replaced with the ant colony optimization rw-optimized back propagation neural network-speed identification and motor’s speed determined using this approach the result is satisfactory.

Tasks and Resources Allocation Approach with Priority Constraints in Cloud Computing

Cloud computing is the most developing technology, which allow users to access data, software and IT services. Cloud systems are characterized by the uncertainty of the resources availability. For that reason, its performance is greatly affected by the applied scheduling and allocation algorithm used to map submitted tasks to resources. This paper introduces a heuristic approach that combine Ant Colony and priority-aware schema to achieve task scheduling and resource allocation in cloud computing environments. The algorithm provides three prioritized levels of quality of services to be employed by users per their demand. A level’s priorities dynamically affect the way tasks are distributed in the system. The resources are allocated using a modified version of Ant Colony Optimization. Results show that the proposed algorithm improves the performance of the system by minimizing makespan, decreasing the degree of imbalance between virtual machines, and enhancing the Cloud’s quality of service by achieving user-priority goals.

Implementation of the enhanced ant colony system algorithm to solve reliable communication network design

The problem of communication design has been defined as one of the problems that belong to the category of NP-hard problem, and the aim of the topological communication network design is to identify component placement locations and connectivity aspects. On the other hand, the Reliable Communication Network Design (RCND) is a popular optimization problem used for maximizing network reliability. In addition, finding an accurate calculation of RCND explains the problem of NP-hard problem. To this end, literature studies suggested various metaheuristic algorithms that have been used as approximation methods to find the best solution to this problem. Some of these algorithms belong to the Evolutionary Algorithms (EAs) category, such as Genetic Algorithms (GAs), and some belong to the Swarm Intelligence Algorithms (SIAs) category, such as Ant Colony Optimization (ACO). However, to the best of our knowledge, the Ant Colony System (ACS) algorithm, which is considered an updated version of ACO, has not yet been used to design reliability-constrained communication network topologies. Therefore, this study aims to apply the updated version of the ACS algorithm for solving RCND in small, medium, and large networks. The proposed algorithm was benchmarked against present state-of-the-art techniques that address this challenge. The research findings show that the proposed algorithm is an optimal solution for a fully connected small network size (n=6, 7, 8, and 9) and it has been achieved as an optimal solution for all not fully connected sets (n=14, 16, and 20). In each case, the results for medium-sized networks were better than the benchmark results

A Novel Hybrid Algorithm of Max-Min Ant System with Quadratic Programming to Solve the Unit Commitment Problem

In this paper, we propose a novel hybrid algorithm based on MAX-MIN Ant System version of ant colony optimization coupled with quadratic programming (MMAS-QP). Quadratic programming is used to optimize the Economic Dispatching process and MMAS for planning the switching schedule of a set of production units. The algorithm is implemented in MATLAB software environment for two systems, one is 4 generating units running for 8 hours, and the other is 10 generating units running for 24 hours. The impact of heuristic parameters on the behavior of the algorithm is highlighted through the parameters setting. Results obtained shows improved solution compared to several methods such as Modified Ant Colony Optimization (MACO), particle Swarm Optimization combined with Lagrange Relaxation (PSO-LR), Swarm and Evolutionary Computation (SEC), Particle Swarm Optimization combined with Genetic Algorithm (PSO-GA). The proposed method improves sufficiently the quality of the solution as well as the execution time.

Collective intelligence evolution using ant colony optimization and neural networks

Recently, theory of collective intelligence (CI) evolution is proposed as a meta algorithm toward artificial general intelligence. But the only implementation of the CI algorithm of the theory is the Monte Carlo tree search (MCTS) used by AlphaZero. Since ant colony optimization (ACO) is an extensively used CI algorithm, it is useful to implement CI evolution using ACO. A genetic version of ACO is adapted to satisfy the CI evolution theory by two methods. One method is realized by using a policy network, namely policy network guided ACO (P-ACO). The other method is realized by using a policy network and a value network, namely policy and value network guided ACO (PV-ACO). Both methods of ACO evolution algorithm are applied to Tic-Tac-Toe and Four in a Row, where traditional ACO played poorly compared to the tree search algorithm, e.g., MCTS. Computational experiments are done to compare both methods with pure ACO and MCTS. As a result, the intelligence level of ACO evolution algorithm quickly exceeds pure ACO and MCTS. In this article, the performance of ACO evolution algorithm is analyzed and the feasibility of applying the CI evolution theory to a specific application is verified.

Dynamic routing with ant system and memory-based decision-making process

Dynamic routing is an essential tool for today’s cities. Dynamic routing problems can be solved by modelling them as dynamic optimization problems (DOPs). DOPs can be solved using Swarm Intelligence and specially ant colony optimization (ACO) algorithms. Although different versions of ACO have already been presented for DOPs, there are still limitations in preventing stagnation and premature convergence and increasing convergence rate. To address these issues, we present an in-memory pheromone trail and an algorithm based on it (named AS-gamma) in the framework of ACO. In-memory pheromone trail is effectively increasing diversity after a change in an environment. Results of experimenting AS-gamma in three scenarios on a real-world transportation network with different simulated traffic conditions demonstrated the effectiveness of the presented in-memory pheromone trail method. The advantages of AS-gamma over three existing DOP algorithms have been illustrated in terms of solutions quality. Offline performance and accuracy measures indicate that AS-gamma faces less stagnation, premature convergence and it is suitable for crowded environments.

A metaheuristic‐based method for replica selection in the Internet of Things

SummaryWith the advent of the Internet of Things (IoT), the count of gadgets connected to the Internet has been increased. IoT, as a modern paradigm, has been used to describe the future in which physical things like RFID tags, sensors, actuators, and cellphones can intermingle for achieving shared purposes. Also, we can employ cloud computing for storing the things' information in the IoT. However, this information has been replicated through the network for increasing availability. In this paper, due to the NP‐hard nature of the replica selection problem, an improved version of ant colony optimization (IACO) has been applied. The impact of pheromone on the chosen path is converted by ants to invert the underlying logic of ACO. Due to the existence of different IoT centers, the IACO has been employed for selecting the replicated data in the IoT where the load balancing among IoT centers has been considered. In this method, an ant chooses the ideal point for its movement; then others may not pass the track that the preceding ants have been passed. The obtained outcomes have shown that the method has outperformed the ACO, HQFR, and RTRM approaches regarding the waiting time and load balancing.

IACS-HCSP: Improved ant colony optimization for large-scale home care scheduling problems

The home care and scheduling problem (HCSP) consists on the design of a set of routes to be used by caregivers that provide daily assistance at specific times to patients located in a definite geographic area. In this study we propose a modified version of Ant Colony Optimization (ACO), called IACS-HCSP, to approach this task. In order to be used in this problem, ACO requires modifications in the problem representation and additional mechanisms to deal with constraints. We propose a dynamic neighborhood graph and an improved method that constructs the solution that improves its exploration capability over deterministic or greedy heuristic methods. This technique has been applied to a very large real world instance of HCSP for which results are available for comparison. IACS-HCSP is able to improve the previous results on this specific instance in terms of cost. At the same time it can be used to help decision making when there is a choice between competing objectives, because it finds a full range of feasible solutions with different equilibria between time and size of the required labor force.

A wrapper-filter feature selection technique based on ant colony optimization

Ant colony optimization (ACO) is a well-explored meta-heuristic algorithm, among whose many applications feature selection (FS) is an important one. Most existing versions of ACO are either wrapper based or filter based. In this paper, we propose a wrapper-filter combination of ACO, where we introduce subset evaluation using a filter method instead of using a wrapper method to reduce computational complexity. A memory to keep the best ants and feature dimension-dependent pheromone update has also been used to perform FS in a multi-objective manner. Our proposed approach has been evaluated on various real-life datasets, taken from UCI Machine Learning repository and NIPS2003 FS challenge, using K-nearest neighbors and multi-layer perceptron classifiers. The experimental outcomes have been compared to some popular FS methods. The comparison of results clearly shows that our method outperforms most of the state-of-the-art algorithms used for FS. For measuring the robustness of the proposed model, it has been additionally evaluated on facial emotion recognition and microarray datasets.

Hybrid ant colony optimization for continuous domains for solving emission and economic dispatch problems

This paper presents a new approach to combined Economic and Emission Dispatch (CEED) problem based on a previous work of us, using a hybrid version of Ant Colony Optimization for Continuous Domains (ACOR) and Pattern Search. The CEED is a conflicting multi-objective optimization problem, in which the objective is to minimize both the economic and emission cost while the power demand (and transmission losses) are met. The Hybrid Ant Colony Optimization for Continuous Domains (PS-ACOR) is presented and modified in order to solve this family of power generation problems. This technique can cope with continuous optimization problems with or without constraints, it can converge to global optimum due to its ability to escape from local optimum combining two powerful optimization techniques. In order to demonstrate its effectiveness several tests conducted to the proposed PS-ACOR algorithm. These series of tests are based on case studies of Economic and Emission Dispatch family problems presented in literature.

Cellular Automata-based Improved Ant Colony-based Optimization Algorithm for mitigating DDoS attacks in VANETs

In Vehicular Ad hoc NETworks (VANETs), reliable data dissemination between vehicular nodes necessitate maximum degree of collaboration as they play a significant role for ensuring the core objective of communication. But the malicious action of vehicular nodes may disrupt the established degree of co-operation as they lead to poor performance in spite of high resource utilization. The malicious activity of vehicular nodes like DDoS attack must be detected and resolved in a potential way by identifying optimal nodes and optimal paths using cellular automata that modifies the search ability in a global way for increasing the speed of convergence. To prevent the search from falling into local optimum point and to enhance the convergence speed, global searching potential, an enhanced version of ant colony optimization (ACO) algorithm called Cellular Automata-based Improved Ant Colony-based Optimization Algorithm (CA-IACOA) is propounded. In CA-IACOA, the update rules and dynamic adaptive adjustment technique in pheromones of the traditional ACO is enhanced to a significant level based three threshold level of tolerance. This enhancement in the adopted pheromones mainly for attaining better quality solution through reliable and dynamic increment of pheromone that considers the past history related to the visited paths of the ant agents. Further significant tradeoff that exists between solving quality and solving efficiency is resolved using updated dynamic evaporation factor strategy that aids in rapid convergence. Furthermore, trusted boundary symmetric mutation strategy is incorporated in CA-IACOA for improving mutation and to strengthen mutation efficiency. The simulation experiments of CA-IACOA infers that they are potential in handling DDoS attacks as they obtain quality solutions in identifying optimal nodes and optimal paths for reliable routing.

Project scheduling for minimizing temporary availability cost of rental resources and tardiness penalty of activities

This paper addresses the resource availability cost problem with rental resources where each activity has a given due date to be completed. In this problem setting, the required resources are temporarily rented to accomplish the corresponding activities where the paid fee for the rental resources depends on duration of their availability. In addition, each activity would be subjected to a tardiness penalty if its finish time surpasses its given due date. A mathematical model is presented for the problem and some features of its solution space are established. Also, a best-performed version of ant colony optimization (ACO) algorithm based on Ant Colony System is developed to tackle this strongly NP-Hard problem. The proposed method consists a new compatible schedule generation scheme, a new resource based heuristic role and an efficient local search. In a comprehensive experimental effort, the proposed parameters-tuned approach is compared with the exact solutions obtained by GAMS on several small-scale instances, while results of a competitive metaheuristic based on Genetic Algorithm are employed to validate the developed ACO algorithm for the large-scale instances. Finally, effectiveness of the proposed ACO is analyzed using statistical tests and the impact of the crucial parameters on the resulting solutions is demonstrated.

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Many applications, related to autonomous mobile robots, require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue and handle cooperatively some distributed targets. This problem is formulated by first defining an optimization model and then considering two sub-problems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit a certain number of robots to deal with the found target together. For this latter purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization, and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete the mission. Simulation results indicate that the firefly-based strategy usually provides superior performance and can reduce the wastage of energy, especially in complex scenarios.

Optimal design of a multi-state system with uncertainty in supplier selection

This paper formulates a bi-objective two-stage stochastic programming model simultaneously for two conditions including a joint modular redundancy allocation, and an imperfect opportunistic maintenance optimization of a multi-state weighted k-out-of-n system. Although categorizing system elements based on cost and reliability characteristics is addressed by literature, this paper categorizes system elements considering the uncertain behavior of suppliers. Further more in this paper a condition based maintenance method is developed considering economic dependence and fixed inspection intervals. The objective is to determine jointly (1) an optimal system structure, (2) an imperfect opportunistic maintenance strategy, and (3) inspection intervals. This proposed approach not only is capable of minimizing the system life cycle cost, but also it addresses maximizing system availability. A three-phase simulation procedure is used to evaluate the performance terms of the studied system for several scenarios. To illustrate the proposed approach an optimal design of a wind farm is also provided. Sensitivity analysis is also investigated in this paper. To obtain Pareto optimal solutions, a multi-objective version of ant colony optimization is used.

Navigation of underwater robot based on dynamically adaptive harmony search algorithm

The current research work has employed an evolutionary based novel navigational strategy to trace the collision free near optimal path for underwater robot in a three-dimensional scenario. The population based harmony search algorithm has been dynamically adapted and used to search next global best pose for underwater robot while obstacle is identified near about robot’s current pose. Each pose is evaluated based on their respective value for objective function which incorporates features of path length minimization as well as obstacle avoidance. Dynamic adaptation of control parameters and new perturbation schemes for solution vectors of harmony search has been proposed to strengthen both exploitation and randomization ability of present search process in a balanced manner. Such adaptive tuning process has found to be more effective for avoiding early convergence during underwater motion in comparison with performances of other popular variants of Harmony Search. The proposed path planning method has also shown better navigational performance in comparison with improved version of ant colony optimization and heuristic potential field method for avoiding static obstacles of different shape and sizes during underwater motion. Simulation studies and corresponding experimental verification for three-dimensional navigation are performed to check the accuracy, robustness and efficiency of proposed dynamically adaptive harmony search algorithm.