Pheromone Values Research Articles

Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem

In this paper, hybrid ant colony optimization (HAntCO) approach in solving multi-skill resource-constrained project scheduling problem (MS-RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with ant colony optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS-RCPSP. Experiments have been performed using artificially created dataset instances based on real-world ones. We published those instances that can be used as a benchmark. Presented results show that ACO-based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.

Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

One of the key factors of a successfully implemented mixed-model line system is considering model sequencing problem as well as the line balancing problem. In the literature, there are many studies, which consider these two tightly interrelated problems individually. However, we integrate the model sequencing problem in the line balancing procedure to obtain a more efficient solution for the problem of Simultaneous Balancing and Sequencing of Mixed-Model Parallel Two-Sided Assembly Lines. A mathematical model is developed to present the problem and a novel agent based ant colony optimisation approach is proposed as the solution method. Different agents interact with each other to find a near optimal solution for the problem. Each ant selects a random behaviour from a predefined list of heuristics and builds a solution using this behaviour as a local search rule along with the pheromone value. Different cycle times are allowed for different two-sided lines located in parallel to each other and this yields a complex problem where different production cycles need to be considered to build a feasible solution. The performance of the proposed approach is tested through a set of test cases. Experimental results indicate that considering model sequencing problem with the line balancing problem together helps minimise line length and total number of required workstations. Also, it is found that the proposed approach outperforms other three heuristics tested.

An Improved Decision Strategy of Network Routing Based on Ant Colony Algorithm

-Aiming at getting a high efficient network routing decision strategy, to settle problems of slow convergence speed and easily to fall into local optimal, this paper proposes a new decision strategy based on ant colony algorithm. Memory device to record each time pheromone value and pheromone differences value of the adjacent times to decide follow the former route or find a new one are the focus of this paper. The new decision technology accelerates the convergence rate, improves network utilization rate and accuracy of network routing.

Security and performance enhancement of AODV routing protocol

SummaryA mobile ad hoc networks (MANET) is a decentralized, self‐organizing, infrastructure‐less network and adaptive gathering of independent mobile nodes. Because of the unique characteristics of MANET, the major issues to develop a routing protocol in MANET are the security aspect and the network performance. In this paper, we propose a new secure protocol called Trust Ad Hoc On‐demand Distance Vector (AODV) using trust mechanism. Communication packets are only sent to the trusted neighbor nodes. Trust calculation is based on the behaviors and activities information of each node. It is divided in to trust global (TG) and trust local (TL). TG is a trust calculation based on the total of received routing packets and the total of sending routing packets. TL is a comparison between total received packets and total forwarded packets by neighbor node from specific nodes. Nodes conclude the total trust level of its neighbors by accumulating the TL and TG values. The performance of Trust AODV is evaluated under denial of service/distributed denial of service (DOS/DDOS) attack using network simulator NS‐2. It is compared with the Trust Cross Layer Secure (TCLS) protocol. Simulation results show that the Trust AODV has a better performance than TCLS protocol in terms of end‐to‐end delay, packet delivery ratio, and overhead. Next, we improve the performance of Trust AODV using ant algorithm. The proposed protocol is called Trust AODV + Ant. The implementation of ant algorithm in the proposed secure protocol is by adding an ant agent to put the positive pheromone in the node if the node is trusted. Ant agent is represented as a routing packet. The pheromone value is saved in the routing table of the node. We modified the original routing table by adding the pheromone value field. The path communication is selected based on the pheromone concentration and the shortest path. Trust AODV + Ant is compared with simple ant routing algorithm (SARA), AODV, and Trust AODV under DOS/DDOS attacks in terms of performance. Simulation results show that the packet delivery ratio and throughput of the Trust AODV increase after using ant algorithm. However, in terms of end‐to‐end delay, there is no significant improvement. Copyright © 2014 John Wiley & Sons, Ltd.

Development of a smart machining system using self-optimizing control

In recent years, due to intense competition through globalization, most manufacturing companies have focused on increasing the added value and reducing the production costs of their products. Inevitably, this has led to the use of advanced technology to carry out manufacturing in an effective and efficient way. Monitoring and control of machining processes are becoming increasingly important for maintaining consistent quality in machined parts. The quality of product can be affected by disturbances during machining process. The paper presents a self-optimizing control system (SOCS) for smart machining that applies information science to enable next-generation quality control, in which the need for expensive post-process inspection is eliminated. In the smart machining system with SOCS, each machine is an autonomous entity. The machining system reacts to disturbances autonomously based on the reaction of each autonomous entity or the cooperation among them. In order to develop the SOCS, the disturbances that happened in the machining shop for manufacturing the clutch housing products were analyzed to classify them and to find out the corresponding management methods such as non-negotiation, negotiation, and rescheduling. To prove that the proposed SOCS is self-monitoring, self-adjusting as well as cooperation, a machining process related to tool conditions was considered in this paper. If the disturbance belongs to the non-negotiation type, for example the tool wear, the machine with SOCS adjusts the cutting parameters in consideration of the amount of tool wear to keep the quality of the machined part. In case the disturbance belongs to the negotiation type such as the tool wear exceeding the allowed limit or tool broken, ant colony inspired cooperation among machines is implemented to find out the most appropriate machine for carrying out the machining operation. The best solution is chosen based on the evaluation of pheromone values of the alternative machines in case many machines satisfy the requirements. The work of the machine in which the disturbance happens is performed at another machine in order to keep the machining system running. The experimental results prove that the mechanism of the proposed SOCS enables the system to adapt to the disturbances successfully.

Enriched ant colony optimization and its application in feature selection

This paper presents a new variant of ant colony optimization (ACO), called enRiched Ant Colony Optimization (RACO). This variation tries to consider the previously traversed edges in the earlier executions to adjust the pheromone values appropriately and prevent premature convergence. Feature selection (FS) is the task of selecting relevant features or disregarding irrelevant features from data. In order to show the efficacy of the proposed algorithm, RACO is then applied to the feature selection problem. In the RACO-based feature selection (RACOFS) algorithm, it might be assumed that the proposed algorithm considers later features with a higher priority. Hence in another variation, the algorithm is integrated with a capability local search procedure to demonstrate that this is not the case. The modified RACO algorithm is able to find globally optimal solutions but suffers from entrapment in local optima. Hence, in the third variation, the algorithm is integrated with a local search procedure to tackle this problem by searching the vicinity of the globally optimal solution. To demonstrate the effectiveness of the proposed algorithms, experiments were conducted using two measures, kappa statistics and classification accuracy, on several standard datasets. The comparisons were made with a wide variety of other swarm-based algorithms and other feature selection methods. The results indicate that the proposed algorithms have superiorities over competitors.

Trusted and load-balanced ant-based routing in mobile ad hoc networks

This paper presents an ant-based trusted routing algorithm with load-balancing for mobile ad hoc networks within the framework of mobile process groups MPGs. The proposed algorithm uses a hybrid approach as it proactively forms the group views of mobile processes agents in initialisation phase and maintains and updates these group views reactively in accordance with the topology change in maintenance phase. Route is discovered reactively on-demand when a source node sends a route request for routing. Reactive path setup is instigated by sending ant packets on the trusted paths. These ant packets update the routing path with a pheromone value which is computed using trust on that node and the node's load taking capability. Therefore, ant packets always follow the path which is most trustworthy and has low traffic load density. The proposed approach was implemented in JADE and the results show that the nodes having high trust and less traffic load density were always chosen as forwarding nodes forming the trusted path with less load.

Adaptive ARA Algorithm (AARA): Proposed Modifications and Experimental Results

Since ARA is a reactive protocol, that is why it is used in such situations where mobility of nodes are higher. In this section, we have proposed the modifications to the algorithm by which the potential of ARA will increase in high mobility scenarios. Pheromone updates play a critical role in the performance of the ant algorithm. In ARA algorithm, initial pheromone value is computed by number hops during the route discovery. This method may not be suitable when nodes are mobile. Pheromone equations are classified in different categories. Two of them are the Classic pheromone filter, where route quality is not taken into consideration, for example the original ARA pheromone equation and the Gamma pheromone filter, which takes time and route quality into consideration.

Implementasi Algoritma MAX-MIN Ant System pada Penjadwalan Mata Kuliah

University Course Timetabling Problem is a problem faced by every university, one of which is Universitas Multimedia Nusantara. Timetabling process is done by allocating time and space so that the whole associated class and course can be implemented. In this paper, the problem will be solved by using MAX-MIN Ant System Algorithm. This algorithm is an alternative approach to ant colony optimization. This algorithm uses two tables of pheromones as stigmergy, i.e. timeslot pheromone table and room pheromone table. In addition, the selection of timeslot and room is done by using the standard deviation of the value of pheromones. Testing is carried out by using 105 events, 45 timeslots, and 3 types of categories based on the number of rooms provided, i.e. large, medium, and small. In each category, testing is performed 5 times and for each testing, the data recorded is the unplace and Soft Constraint Penalty. In general, the greater the number of rooms, the smaller the unplace.
 Index Terms—ant colony optimization, max-min ant system, timetabling

The Effect of Pheromone in Ant-Based Hyper-Heuristic

-Ant algorithms mimic the behavior of the ants where their important behavior is the ability to find the shortest path between food sources and their nest despite being almost blind. In the algorithms, as ants travel, they deposit a chemical substance called pheromone which; together with visibility values is used to make decisions. This paper investigates the effects of pheromone values on solving a routing problem; the capacitated vehicle routing problem (CVRP) In our approach, in order to produce a generalized approach, we developed an ant-based hyper-heuristic where pheromone and visibility values consider a non-domain specific knowledge. In this paper, we propose to provide all visited heuristics with some amount of pheromone. The distribution of pheromone values will be distributed proportioned to the performance done by the ants. This is to encourage the exploration of new edges that might lead to better solutions. We show that our results are better when compared to two other ant algorithm hyper-heuristics in the literature.

A Comparatives Study of Gateway Placement Optimization in Wireless Mesh Network using GA, PSO and ACO

In this paper, I study the challenging problem of optimizing gateway placement for throughput in Wireless Mesh Networks. I compare my study ofGenetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) to solve the optimal gateway placement in Wireless Mesh Networks problem. I generate the locations of gateway randomly and independently and choose the router with the highest weight as potential location for gateway placement use multi-traffic-flow weight. In the my algorithms based on GA and PSO, I calculate the fitness value of each scheme and update them step by step with the best method to quickly find the optimal. With ACO algorithm, I calculate the probability and pheromone values of ants will choose to go from current gateway i to next client j. After each iteration, the pheromone values are updated by all the number of ants that have reached to the destination successfully and found the optimal solution.Numerical results show that the proposed algorithm has achieved much better than previous studies.

Improved ACO Algorithm with Pheromone Correction Strategy for the Traveling Salesman Problem

A new, improved ant colony optimization (ACO) algorithm with novel pheromone correction strategy is introduced. It is implemented and tested on the traveling salesman problem (TSP). Algorithm modification is based on undesirability of some elements of the current best found solution. The pheromone values for highly undesirable links are significantly lowered by this a posteriori heuristic. This new hybridized algorithm with the strategy for avoiding stagnation by leaving local optima was tested on standard benchmark problems from the TSPLIB library and superiority of our method to the basic ACO and also to the particle swarm optimization (PSO) is shown. The best found solutions are improved, as well as the mean values for multiple runs. The computation cost increase for our modification is negligible.

Ant Colony System with Heuristic Function for the Travelling Salesman Problem

Abstract Ant colony system which is classified as a meta-heuristic algorithm is considered as one of the best optimization algorithm for solving different type of NP-Hard problem including the travelling salesman problem. A heuristic function in the Ant colony system uses pheromone and distance values to produce heuristic values in solving the travelling salesman problem. However, the heuristic values are not updated in the entire process to reflect the knowledge discovered by ants while moving from city to city. This paper presents the work on enhancing the heuristic function in ant colony system in order to reflect the new information discovered by the ants. Experimental results showed that enhanced algorithm provides better results than classical ant colony system in term of best, average and standard of the best tour length. Keywords : Ant Colony Optimization, Ant Colony System, Heuristic Function, Traveling Salesman Problem 1. Introduction Biological ants have the ability to discover the shortest route from the nest to the source of food [1]. Although they do not have an advanced vision system [2], they have the ability to communicate with the environment. Ants use a chemical substance called a “pheromone” to communicate with the environment and between each other [3]. Pheromone substance has an evaporation property which is a powerful mechanism to update the route information. While an ant moves looking for food, it deposits a pheromone along the path. The following ant will, more likely, select the route with richer pheromones. This mechanism will make the ant choose the shortest path. In 1992, Marco Dorigo proposed the first Ant Colony Optimization (ACO) algorithm to search for an optimal solution in graphs to solve optimization problems such as the travelling salesman problem, job scheduling and network routing [1]. The variants of ACO are: (i) Ant System (AS) [4] [5] [6]. (ii) The first improvement on the ant system, called the Elitist strategy for Ant System (EAS) [7]. The improvement was done by providing strong additional reinforcement to the arcs belonging to the best tour found since the start of the algorithm. (iii) Rank-Based Ant System (AS

A hybrid multi-path routing algorithm for industrial wireless mesh networks

Abstract Multi-path routing, a routing technique that enables data transmission over multiple paths, is an effective strategy in achieving reliability in wireless sensor networks. However, multi-path routing does not guarantee deterministic transmission. This is because more than one path is available for transferring data from the source node to the destination node. A hybrid multi-path routing algorithm is proposed for industrial wireless mesh networks for improving reliability and determinacy of data transmission, as well as to effectively deal with link failures. The proposed algorithm adopts the enhanced Dijkstra’s algorithm for searching the shortest route from the gateway to each end node for first route setup. A virtual pheromone distinct from the regular pheromone is introduced to realize pheromone diffusion and updating. In this way, multiple routes are searched based on the ant colony optimization algorithm. The routes used for data transmission are selected based on their regular pheromone values, facilitating the delivery of data through better routes. Link failures are then handled using route maintenance mechanism. Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of average end-to-end delay, packet delivery ratio, and routing overhead; moreover, it has a strong capacity to cope with topological changes, thereby making it more suitable for industrial wireless mesh networks.

A New Metaheuristic Algorithm for Long-Term Open-Pit Production Planning / Nowy meta-heurystyczny algorytm wspomagający długoterminowe planowanie produkcji w kopalni odkrywkowej

Paper describes a new metaheuristic algorithm which has been developed based on the Ant Colony Optimisation (ACO) and its efficiency have been discussed. To apply the ACO process on mine planning problem, a series of variables are considered for each block as the pheromone trails that represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. During implementation several mine schedules are constructed in each iteration. Then the pheromone values of all blocks are reduced to a certain percentage and additionally the pheromone value of those blocks that are used in defining the constructed schedules are increased according to the quality of the generated solutions. By repeated iterations, the pheromone values of those blocks that define the shape of the optimum solution are increased whereas those of the others have been significantly evaporated.

Hybridization of Harmony Search and Ant Colony Optimization for optimal locating of structural dampers

This paper proposes a hybrid algorithm to find optimal locations for dampers within a structural system. In this approach Harmony Search is augmented by a probability mass function which utilizes two concepts borrowed from Ant Colony Optimization algorithm: pheromone and heuristic values. The former is a dynamic weight factor assigned to each solution component and the latter is a constant one defined based on modal analysis of the structural system. Provided numerical examples show that this hybrid search scheme improves the convergence rate such that the algorithm finds qualified solutions in fewer number of iterations.

A New Sequential Covering Strategy for Inducing Classification Rules With Ant Colony Algorithms

Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction, i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using 18 publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms.

Ant Colony Optimization with Re-Initialization

This contribution introduces an Ant Colony Optimization (ACO) algorithm with re-initialization mechanism. The whole search process is broken by re-initialization into shorter semi-independent steps called “macro cycles”. The length of macro cycle depends on pheromone accumulation and can be adjusted by a user parameter. It is shown that re-initialization mechanism prevents ACO algorithm from pheromone saturation and consecutive stagnation. This approach avoids overhead caused by algorithm run with excessive pheromone values where further exploration is hardly possible. The solution offers lower CPU cost of the search process and enables automation of heuristic search especially in changing environments like dynamic networks. The efficiency of proposed method is demonstrated on a path minimization problem on 50 node graph.

Ant Colony Optimization with Genetic Operations

This paper attempts to overcome stagnation problem of Ant Colony Optimization (ACO) algorithms. Stagnation is undesirable state which occurs at a later phases of the search process. Excessive pheromone values attract more ants and make further exploration hardly possible. This problem has been addressed by Genetic operations (GO) incorporated into ACO framework. Crossover and mutation operations have been adapted for use with ant generated strings which still have to provide feasible solutions. Genetic operations decrease selection pressure and increase probability of finding the global optimum. Extensive simulation tests were made in order to determine influence of genetic operation on algorithm performance.

Scheduling Multi-Mode Projects under Uncertainty to Optimize Cash Flows: A Monte Carlo Ant Colony System Approach

Project scheduling under uncertainty is a challenging field of research that has attracted increasing attention. While most existing studies only consider the single-mode project scheduling problem under uncertainty, this paper aims to deal with a more realistic model called the stochastic multi-mode resource constrained project scheduling problem with discounted cash flows (S-MRCPSPDCF). In the model, activity durations and costs are given by random variables. The objective is to find an optimal baseline schedule so that the expected net present value (NPV) of cash flows is maximized. To solve the problem, an ant colony system (ACS) based approach is designed. The algorithm dispatches a group of ants to build baseline schedules iteratively using pheromones and an expected discounted cost (EDC) heuristic. Since it is impossible to evaluate the expected NPV directly due to the presence of random variables, the algorithm adopts the Monte Carlo (MC) simulation technique. As the ACS algorithm only uses the best-so-far solution to update pheromone values, it is found that a rough simulation with a small number of random scenarios is enough for evaluation. Thus the computational cost is reduced. Experimental results on 33 instances demonstrate the effectiveness of the proposed model and the ACS approach.