scholarly journals GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

2021 ◽  
Vol 7 ◽  
pp. e729
Author(s):  
Mulki Indana Zulfa ◽  
Rudy Hartanto ◽  
Adhistya Erna Permanasari ◽  
Waleed Ali
Keyword(s):  
Objective Function ◽  
Ant Colony Optimization ◽  
Data Exchange ◽  
Edge Computing ◽  
Ant Colony ◽  
Solution Quality ◽  
Selection Probability ◽  
Time Consumption ◽  
Solution Selection ◽  
Data Optimization

Background Data exchange and management have been observed to be improving with the rapid growth of 5G technology, edge computing, and the Internet of Things (IoT). Moreover, edge computing is expected to quickly serve extensive and massive data requests despite its limited storage capacity. Such a situation needs data caching and offloading capabilities for proper distribution to users. These capabilities also need to be optimized due to the experience constraints, such as data priority determination, limited storage, and execution time. Methods We proposed a novel framework called Genetic and Ant Colony Optimization (GenACO) to improve the performance of the cached data optimization implemented in previous research by providing a more optimum objective function value. GenACO improves the solution selection probability mechanism to ensure a more reliable balancing of the exploration and exploitation process involved in finding solutions. Moreover, the GenACO has two modes: cyclic and non-cyclic, confirmed to have the ability to increase the optimal cached data solution, improve average solution quality, and reduce the total time consumption from the previous research results. Result The experimental results demonstrated that the proposed GenACO outperformed the previous work by minimizing the objective function of cached data optimization from 0.4374 to 0.4350 and reducing the time consumption by up to 47%.

Optimization of Multi-Depot Heterogeneous Incident Vehicle Routing Problem with Soft Time Windows Based on an Improved Ant Colony Optimization

2014 ◽  
Vol 1061-1062 ◽  
pp. 1108-1117
Author(s):  
Ya Lian Tang ◽  
Yan Guang Cai ◽  
Qi Jiang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Ant Colony ◽  
Genetic Operators ◽  
Solution Quality ◽  
Routing Problem ◽  
Soft Time Windows ◽  
Heterogeneous Vehicle Routing Problem

Aiming at vehicle routing problem (VRP) with many extended features is widely used in actual life, multi-depot heterogeneous vehicle routing problem with soft time windows (MDHIVRPSTW) mathematical model is established. An improved ant colony optimization (IACO) is proposed for solving this model. Firstly, MDHIVRPSTW was transferred into different groups according to nearest depot method, then constructing the initial route by scanning algorithm (SA). Secondly, genetic operators were introduced, and then adjusting crossover probability and mutation probability adaptively in order to improve the global search ability of the algorithm. Moreover, smooth mechanism was used to improve the performance of ant colony optimization (ACO). Finally, 3-opt strategy was used to improve the local search ability. The proposed IACO has been tested on a 32-customer instance which was generated randomly. The experimental results show that IACO is superior to other three algorithms in terms of convergence speed and solution quality, thus the proposed method is effective and feasible, and the proposed model is better than conventional model.

Quantifying Ordinal Performance of Ant Colony Optimization

2013 ◽  
Vol 378 ◽  
pp. 387-393
Author(s):  
Zhao Jun Zhang ◽  
Zu Ren Feng
Keyword(s):  
Ant Colony Optimization ◽  
Quality Evaluation ◽  
Feasible Solution ◽  
Theoretical Foundation ◽  
Search Space ◽  
Ant Colony ◽  
Analysis Method ◽  
Solution Quality ◽  
Intelligent Optimization Algorithms ◽  
Feasible Solutions

In contrast to many successful applications of ant colony optimization, the theoretical foundation is rather weak. It greatly limits the application in practical problems. One problem, called solution quality evaluation, is how to quantify the performance of the algorithm. It is hardly solved by theoretical methods. Experimental analysis method based on the analysis of search space and characteristic of algorithm itself is proposed in this paper. As algorithm runs, it would produce a large number of feasible solutions. After preprocessing, they were clustered according to distance. Then, good enough set was partitioned by the results of clustering. Last, evaluation result of ordinal performance was got by using relative knowledge of statistics. As the method only uses feasible solution produced by optimization algorithm, it is independent to specific algorithm. Therefore, the proposed method can be adopted by other intelligent optimization algorithms. The method is demonstrated through traveling salesman problem.

scholarly journals An Ant Colony Optimization Based on Information Entropy for Constraint Satisfaction Problems

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 766 ◽  
Author(s):  
Boxin Guan ◽  
Yuhai Zhao ◽  
Yuan Li
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Constraint Satisfaction ◽  
Information Entropy ◽  
Hypothesis Test ◽  
Constraint Satisfaction Problem ◽  
Constraint Satisfaction Problems ◽  
Ant Colony ◽  
Cost Comparison ◽  
Solution Quality

Solving the constraint satisfaction problem (CSP) is to find an assignment of values to variables that satisfies a set of constraints. Ant colony optimization (ACO) is an efficient algorithm for solving CSPs. However, the existing ACO-based algorithms suffer from the constructed assignment with high cost. To improve the solution quality of ACO for solving CSPs, an ant colony optimization based on information entropy (ACOE) is proposed in this paper. The proposed algorithm can automatically call a crossover-based local search according to real-time information entropy. We first describe ACOE for solving CSPs and show how it constructs assignments. Then, we use a ranking-based strategy to update the pheromone, which weights the pheromone according to the rank of these ants. Furthermore, we introduce the crossover-based local search that uses a crossover operation to optimize the current best assignment. Finally, we compare ACOE with seven algorithms on binary CSPs. The experimental results revealed that our method outperformed the other compared algorithms in terms of the cost comparison, data distribution, convergence performance, and hypothesis test.

scholarly journals Complete Inverse Method Using Ant Colony Optimization Algorithm for Structural Parameters and Excitation Identification from Output Only Measurements

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Jun Chen ◽  
Xin Chen ◽  
Wei Liu
Keyword(s):  
Structural Health Monitoring ◽  
Objective Function ◽  
Ant Colony Optimization ◽  
Health Monitoring ◽  
Inverse Method ◽  
Structural Parameters ◽  
Ant Colony ◽  
Computation Method ◽  
Structural Health ◽  
Output Only

In vibration-based structural health monitoring of existing large civil structures, it is difficult, sometimes even impossible, to measure the actual excitation applied to structures. Therefore, an identification method using output-only measurements is crucial for the practical application of structural health monitoring. This paper integrates the ant colony optimization (ACO) algorithm into the framework of the complete inverse method to simultaneously identify unknown structural parameters and input time history using output-only measurements. The complete inverse method, which was previously suggested by the authors, converts physical or spatial information of the unknown input into the objective function of an optimization problem that can be solved by the ACO algorithm. ACO is a newly developed swarm computation method that has a very good performance in solving complex global continuous optimization problems. The principles and implementation procedure of the ACO algorithm are first introduced followed by an introduction of the framework of the complete inverse method. Construction of the objective function is then described in detail with an emphasis on the common situation wherein a limited number of actuators are installed on some key locations of the structure. Applicability and feasibility of the proposed method were validated by numerical examples and experimental results from a three-story building model.

scholarly journals Dynamic Flying Ant Colony Optimization (DFACO) for Solving the Traveling Salesman Problem

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1837 ◽  
Author(s):  
Dahan ◽  
El Hindi ◽  
Mathkour ◽  
AlSalman
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Service Selection ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Neighboring Node ◽  
Solution Quality ◽  
Web Service Selection ◽  
The Traveling Salesman Problem

This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to solve the quality of service (QoS)-aware web service selection problem. Many researchers have addressed the TSP, but most solutions could not avoid the stagnation problem. In FACO, a flying ant deposits a pheromone by injecting it from a distance; therefore, not only the nodes on the path but also the neighboring nodes receive the pheromone. The amount of pheromone a neighboring node receives is inversely proportional to the distance between it and the node on the path. In this work, we modified the FACO algorithm to make it suitable for TSP in several ways. For example, the number of neighboring nodes that received pheromones varied depending on the quality of the solution compared to the rest of the solutions. This helped to balance the exploration and exploitation strategies. We also embedded the 3-Opt algorithm to improve the solution by mitigating the effect of the stagnation problem. Moreover, the colony contained a combination of regular and flying ants. These modifications aim to help the DFACO algorithm obtain better solutions in less processing time and avoid getting stuck in local minima. This work compared DFACO with (1) ACO and five different methods using 24 TSP datasets and (2) parallel ACO (PACO)-3Opt using 22 TSP datasets. The empirical results showed that DFACO achieved the best results compared with ACO and the five different methods for most of the datasets (23 out of 24) in terms of the quality of the solutions. Further, it achieved better results compared with PACO-3Opt for most of the datasets (20 out of 21) in terms of solution quality and execution time.

scholarly journals Determining the Optimum Section of Tunnels Using Ant Colony Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
S. Talatahari
Keyword(s):  
Objective Function ◽  
Ant Colony Optimization ◽  
Cross Sections ◽  
Optimization Methods ◽  
Ant Colony ◽  
Great Role ◽  
Numerical Examples ◽  
First Time

Ant colony optimization is developed to determine optimum cross sections of tunnel structures. Tunnel structures are expensive infrastructures in terms of material, construction, and maintenance and the application of optimization methods has a great role in minimizing their costs. This paper presents the formulation of objective function and constraints of the problem for the first time, and the ant colony optimization, as a developed metaheuristic approach, has been used to solve the problem. The results and comparisons based on numerical examples show the efficiency of the algorithm.

A Maturity-Based Adaptive Ant Colony Optimization Algorithm

2011 ◽  
Vol 50-51 ◽  
pp. 353-357
Author(s):  
Hai Ning Wang ◽  
Shou Qian Sun ◽  
Bo Liu
Keyword(s):  
Convergence Rate ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Solution Quality ◽  
Local Optima ◽  
The Relationship

In this paper, for the problems of low convergence rate and getting trapped in local optima easily, the average path similarity (APS) was proposed to present the optimization maturity by analyzing the relationship between parameters of local pheromone updating and global pheromone updating, as well as the optimizing capacity and convergence rate. Furthermore, the coefficients of pheromone updating adaptively were adjusted to improve the convergence rate and prevent the algorithm from getting stuck in local optima. The adaptive ACS has been applied to optimize several benchmark TSP instances. The solution quality and convergence rate of the algorithm were compared comprehensively with conventional ACS to verify the validity and the effectiveness.

Use of Ant Colony Optimization (ACO) for Post-Deployment Replanning of UMTS Networks

2018 ◽  
Vol 9 (2) ◽  
pp. 18-47
Author(s):  
Samir K Sadhukhan ◽  
Chayanika Bose ◽  
Debashis Saha
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony ◽  
Mobility Patterns ◽  
Solution Quality ◽  
Integer Linear Programming Problem ◽  
State Space Search ◽  
Link Cost ◽  
Diurnal Movement ◽  
Umts Networks

Inherent dynamism in user movement demands for post-deployment tuning of UMTS networks to minimize the total cost of ownership (TCO). Conventionally, UMTS operators so far have considered many-to-one mapping of RNCs to MSCs. However, such single-homed networks do not remain cost-effective over the passage of time, typically when subscribers later on begin to show specific mobility patterns such as diurnal movement. This necessitates topological extension of the network in terms of dual-homing of some selected RNCs to two MSCs simultaneously via direct fibre links, resulting in a many-to-two mapping in parts of the network. The aim of such selective dual-homing is to reduce handoff cost maximally at the expense of minimal increase in link cost, thereby reducing the TCO optimally. In this article, the authors have formulated the scenario as an integer linear programming problem, converted it into a state space search and then solved it using Ant Colony Optimization (ACO) technique. Compared to Simulated Annealing (SA) and Tabu Search (TS), ACO exhibits 10% to 15% improvement in solution quality.

Automatic Generation Control of Thermal-Thermal-Hydro Power Systems with PID Controller Using Ant Colony Optimization

2017 ◽  
pp. 761-778 ◽  
Author(s):  
Jagatheesan Kaliannan ◽  
Anand Baskaran ◽  
Nilanjan Dey
Keyword(s):  
Power Systems ◽  
Power System ◽  
Objective Function ◽  
Ant Colony Optimization ◽  
Pid Controller ◽  
Pi Controller ◽  
Ant Colony ◽  
Hydro Power ◽  
Proportional Integral ◽  
Hydro Power Plants

In this work, Artificial Intelligence (AI) based Ant Colony Optimization (ACO) algorithm is proposed for Load Frequency Control (LFC) of interconnected multi–area hydrothermal power systems. Area 1&2 are thermal power systems and area 3 is a hydro power system, all the areas are interconnected through the appropriate tie-line. Thermal and hydro power plants are applied with reheat turbine and electric governor respectively. Investigated power system initially applied with conventional Proportional-Integral (PI) controller and controller parameters are optimized by using trial and error method considering Integral Time Absolute Error (ITAE) objective function. After that, the system is equipped with Proportional – Integral – Derivative (PID) controller and controller parameters are optimized by using ACO algorithm with ITAE objective function. The superiority of the proposed algorithm has been demonstrated by comparing conventional controller. Finally, The Simulation results of multi-area power system prove the effectiveness of the proposed optimization technique in LFC scheme and show its superiority over conventional PI controller.

scholarly journals An Integrated Metaheuristic Routing Method for Multiple-Block Warehouses with Ultranarrow Aisles and Access Restriction

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Fangyu Chen ◽  
Gangyan Xu ◽  
Yongchang Wei
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Hybrid Algorithm ◽  
Routing Algorithm ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Service Efficiency ◽  
Routing Method ◽  
Simulation Results

A problem-specific routing algorithm integrating ant colony optimization (ACO) and integer-coded genetic algorithm (GA) is developed to address the newly observed limitations imposed by ultranarrow aisles and access restriction, which exist in the largest e-commerce enterprise with self-run logistics in China. Those limitations prohibit pickers from walking through the whole aisle, and the access restriction even allows them to access the pick aisles only from specific entrances. The ant colony optimization is mainly responsible for generating the initial chromosomes for the genetic algorithm, which then searches the near-optimal solutions of picker-routing with our novel chromosome design by recording the detailed information of access modes and subaisles. To demonstrate the merits of the proposed algorithm, a comprehensive simulation for comparison is conducted with 12 warehouse layouts with real order information. The simulation results show that the proposed hybrid algorithm is superior to dedicated heuristics in terms of solution quality. The impacts of the parameters with respect to warehouse layout on the picking efficiency are analyzed as well. Setting more connect aisles and cross aisles is suggested to effectively optimize the picking-service efficiency in the presence of access limitations.

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