Abstract: This paper mainly studies the path planning method based on ant colony optimization (ACO), and proposes heuristic integration ant colony optimization (HIACO). This paper not only analyzes and optimizes the principle, but also simulates and analyzes the parameters related to the application of HIACO in path planning. Compared with the original algorithm, the improved algorithm optimizes probability formula, tabu table mechanism and updating mechanism, and introduces more reasonable heuristic factors. The optimized HIACO not only draws on the excellent ideas of the original algorithm, but also solves the problems of premature convergence, convergence to the sub optimal solution and improper exploration to some extent. HIACO can be used to achieve better simulation results and achieve the desired optimization. Combined with the probability formula and update formula, several parameters of HIACO are tested. This paper proves the principle of the HIACO and gives the best parameter range in the research of path planning.
Abstract: Motion planning is a common task required to be fulfilled by robots. A strategy combining Ant Colony Optimization (ACO) and gravity gradient inversion algorithm is proposed for motion planning of mobile robots. In this paper, in order to realize optimal motion planning strategy, the cost function in ACO is designed based on gravity gradient inversion algorithm. The obstacles around mobile robot can cause gravity gradient anomalies; the gradiometer is installed on the mobile robot to detect the gravity gradient anomalies. After obtaining the anomalies, gravity gradient inversion algorithm is employed to calculate relative distance and orientation between mobile robot and obstacles. The relative distance and orientation deduced from gravity gradient inversion algorithm is employed as cost function in ACO algorithm to realize motion planning. The proposed strategy is validated by the simulation and experiment results.
Abstract: A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. Support Vector Machine (SVM), which originates from the statistical approach, is a present day classification technique. The main problems of SVM are selecting feature subset and tuning the parameters. Discretizing the continuous value of the parameters is the most common approach in tuning SVM parameters. This process will result in loss of information which affects the classification accuracy. This paper presents two algorithms that can simultaneously tune SVM parameters and select the feature subset. The first algorithm, ACOR-SVM, will tune SVM parameters, while the second IACOMV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. Three benchmark UCI datasets were used in the experiments to validate the performance of the proposed algorithms. The results show that the proposed algorithms have good performances as compared to other approaches.
Abstract: This paper describes an approach to detect the
transmitted signals for 2×2 Multiple Input Multiple Output (MIMO)
setup using roulette wheel based ant colony optimization technique.
The results obtained are compared with classical zero forcing and
least mean square techniques. The detection rates achieved using
this technique are consistently larger than the one achieved using
classical methods for 50 number of attempts with two different
antennas transmitting the input stream from a user. This paves the
path to use alternative techniques to improve the throughput achieved
in advanced networks like Long Term Evolution (LTE) networks.
Abstract: Carefully scheduling the operations of pumps can be
resulted to significant energy savings. Schedules can be defined
either implicit, in terms of other elements of the network such as tank
levels, or explicit by specifying the time during which each pump is
on/off. In this study, two new explicit representations based on timecontrolled
triggers were analyzed, where the maximum number of
pump switches was established beforehand, and the schedule may
contain fewer switches than the maximum. The optimal operation of
pumping stations was determined using a Jumping Particle Swarm
Optimization (JPSO) algorithm to achieve the minimum energy cost.
The model integrates JPSO optimizer and EPANET hydraulic
network solver. The optimal pump operation schedule of VanZyl
water distribution system was determined using the proposed model
and compared with those from Genetic and Ant Colony algorithms.
The results indicate that the proposed model utilizing the JPSO
algorithm is a versatile management model for the operation of realworld
water distribution system.
Abstract: Ant Colony Optimization (ACO) is a promising
modern approach to the unused combinatorial optimization. Here
ACO is applied to finding the shortest during communication link
failure. In this paper, the performances of the prim’s and ACO
algorithm are made. By comparing the time complexity and program
execution time as set of parameters, we demonstrate the pleasant
performance of ACO in finding excellent solution to finding shortest
path during communication link failure.
Abstract: This paper presents the performance of Integrated
Bacterial Foraging Optimization and Particle Swarm Optimization
(IBFO_PSO) technique in MANET routing. The BFO is a bio-inspired
algorithm, which simulates the foraging behavior of bacteria.
It is effectively applied in improving the routing performance in
MANET. In results, it is proved that the PSO integrated with BFO
reduces routing delay, energy consumption and communication
overhead.
Abstract: Evolutionary optimization methods such as genetic
algorithms have been used extensively for the construction site layout
problem. More recently, ant colony optimization algorithms, which
are evolutionary methods based on the foraging behavior of ants,
have been successfully applied to benchmark combinatorial
optimization problems. This paper proposes a formulation of the site
layout problem in terms of a sequencing problem that is suitable for
solution using an ant colony optimization algorithm.
In the construction industry, site layout is a very important
planning problem. The objective of site layout is to position
temporary facilities both geographically and at the correct time such
that the construction work can be performed satisfactorily with
minimal costs and improved safety and working environment. During
the last decade, evolutionary methods such as genetic algorithms
have been used extensively for the construction site layout problem.
This paper proposes an ant colony optimization model for
construction site layout. A simple case study for a highway project is
utilized to illustrate the application of the model.
Abstract: Nature is the immense gifted source for solving
complex problems. It always helps to find the optimal solution to
solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide
research area of networks which has set of independent nodes. The
characteristics involved in MANET’s are Dynamic, does not depend
on any fixed infrastructure or centralized networks, High mobility.
The Bio-Inspired algorithms are mimics the nature for solving
optimization problems opening a new era in MANET. The typical
Swarm Intelligence (SI) algorithms are Ant Colony Optimization
(ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization
(PSO), Modified Termite Algorithm, Bat Algorithm (BA), Wolf
Search Algorithm (WSA) and so on. This work mainly concentrated
on nature of MANET and behavior of nodes. Also it analyses various
performance metrics such as throughput, QoS and End-to-End delay
etc.
Abstract: Nature is a great source of inspiration for solving
complex problems in networks. It helps to find the optimal solution.
Metaheuristic algorithm is one of the nature-inspired algorithm which
helps in solving routing problem in networks. The dynamic features,
changing of topology frequently and limited bandwidth make the
routing, challenging in MANET. Implementation of appropriate
routing algorithms leads to the efficient transmission of data in
mobile ad hoc networks. The algorithms that are inspired by the
principles of naturally-distributed/collective behavior of social
colonies have shown excellence in dealing with complex
optimization problems. Thus some of the bio-inspired metaheuristic
algorithms help to increase the efficiency of routing in ad hoc
networks. This survey work presents the overview of bio-inspired
metaheuristic algorithms which support the efficiency of routing in
mobile ad hoc networks.
Abstract: Every machine plays roles of client and server
simultaneously in a peer-to-peer (P2P) network. Though a P2P
network has many advantages over traditional client-server models
regarding efficiency and fault-tolerance, it also faces additional
security threats. Users/IT administrators should be aware of risks
from malicious code propagation, downloaded content legality, and
P2P software’s vulnerabilities. Security and preventative measures
are a must to protect networks from potential sensitive information
leakage and security breaches. Bit Torrent is a popular and scalable
P2P file distribution mechanism which successfully distributes large
files quickly and efficiently without problems for origin server. Bit
Torrent achieved excellent upload utilization according to
measurement studies, but it also raised many questions as regards
utilization in settings, than those measuring, fairness, and Bit
Torrent’s mechanisms choice. This work proposed a block selection
technique using Fuzzy ACO with optimal rules selected using ACO.
Abstract: The objective of the Economic Dispatch(ED) Problems
of electric power generation is to schedule the committed generating
units outputs so as to meet the required load demand at minimum
operating cost while satisfying all units and system equality and
inequality constraints. This paper presents a new method of ED
problems utilizing the Max-Min Ant System Optimization.
Historically, traditional optimizations techniques have been used,
such as linear and non-linear programming, but within the past
decade the focus has shifted on the utilization of Evolutionary
Algorithms, as an example Genetic Algorithms, Simulated Annealing
and recently Ant Colony Optimization (ACO). In this paper we
introduce the Max-Min Ant System based version of the Ant System.
This algorithm encourages local searching around the best solution
found in each iteration. To show its efficiency and effectiveness, the
proposed Max-Min Ant System is applied to sample ED problems
composed of 4 generators. Comparison to conventional genetic
algorithms is presented.
Abstract: Segmentation is one of the essential tasks in image
processing. Thresholding is one of the simplest techniques for
performing image segmentation. Multilevel thresholding is a simple
and effective technique. The primary objective of bi-level or
multilevel thresholding for image segmentation is to determine a best
thresholding value. To achieve multilevel thresholding various
techniques has been proposed. A study of some nature inspired
metaheuristic algorithms for multilevel thresholding for image
segmentation is conducted. Here, we study about Particle swarm
optimization (PSO) algorithm, artificial bee colony optimization
(ABC), Ant colony optimization (ACO) algorithm and Cuckoo
search (CS) algorithm.
Abstract: Genome rearrangement is an important area in computational biology and bioinformatics. The basic problem in genome rearrangements is to compute the edit distance, i.e., the minimum number of operations needed to transform one genome into another. Unfortunately, unsigned genome rearrangement problem is NP-hard. In this study an improved ant colony optimization algorithm to approximate the edit distance is proposed. The main idea is to convert the unsigned permutation to signed permutation and evaluate the ants by using Kaplan algorithm. Two new operations are added to the standard ant colony algorithm: Replacing the worst ants by re-sampling the ants from a new probability distribution and applying the crossover operations on the best ants. The proposed algorithm is tested and compared with the improved breakpoint reversal sort algorithm by using three datasets. The results indicate that the proposed algorithm achieves better accuracy ratio than the previous methods.
Abstract: This paper presents an improved ant colony optimization (IACO) for solving the reliability redundancy allocation problem (RAP) in order to maximize system reliability. To improve the performance of ACO algorithm, two additional techniques, i.e. neighborhood search, and re-initialization process are presented. To show its efficiency and effectiveness, the proposed IACO is applied to solve three RAPs. Additionally, the results of the proposed IACO are compared with those of the conventional heuristic approaches i.e. genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). The experimental results show that the proposed IACO approach is comparatively capable of obtaining higher quality solution and faster computational time.
Abstract: The problem of optimal planning of multiple sources
of distributed generation (DG) in distribution networks is treated in
this paper using an improved Ant Colony Optimization algorithm
(ACO). This objective of this problem is to determine the DG
optimal size and location that in order to minimize the network real
power losses. Considering the multiple sources of DG, both size and
location are simultaneously optimized in a single run of the proposed
ACO algorithm. The various practical constraints of the problem are
taken into consideration by the problem formulation and the
algorithm implementation. A radial power flow algorithm for
distribution networks is adopted and applied to satisfy these
constraints. To validate the proposed technique and demonstrate its
effectiveness, the well-know 69-bus feeder standard test system is
employed.cm.
Abstract: Total weighted tardiness is a measure of customer
satisfaction. Minimizing it represents satisfying the general
requirement of on-time delivery. In this research, we consider an ant
colony optimization (ACO) algorithm to solve the problem of
scheduling unrelated parallel machines to minimize total weighted
tardiness. The problem is NP-hard in the strong sense. Computational
results show that the proposed ACO algorithm is giving promising
results compared to other existing algorithms.
Abstract: Ants are fascinating creatures that demonstrate the
ability to find food and bring it back to their nest. Their ability as a
colony, to find paths to food sources has inspired the development of
algorithms known as Ant Colony Systems (ACS). The principle of
cooperation forms the backbone of such algorithms, commonly used
to find solutions to problems such as the Traveling Salesman
Problem (TSP). Ants communicate to each other through chemical
substances called pheromones. Modeling individual ants- ability to
manipulate this substance can help an ACS find the best solution.
This paper introduces a Dynamic Ant Colony System with threelevel
updates (DACS3) that enhance an existing ACS. Experiments
were conducted to observe single ant behavior in a colony of
Malaysian House Red Ants. Such behavior was incorporated into the
DACS3 algorithm. We benchmark the performance of DACS3 versus
DACS on TSP instances ranging from 14 to 100 cities. The result
shows that the DACS3 algorithm can achieve shorter distance in
most cases and also performs considerably faster than DACS.
Abstract: The expectation of network performance from the
early days of ARPANET until now has been changed significantly.
Every day, new advancement in technological infrastructure opens
the doors for better quality of service and accordingly level of
perceived quality of network services have been increased over the
time. Nowadays for many applications, late information has no value
or even may result in financial or catastrophic loss, on the other hand,
demands for some level of guarantee in providing and maintaining
quality of service are ever increasing. Based on this history, having a
QoS aware routing system which is able to provide today's required
level of quality of service in the networks and effectively adapt to the
future needs, seems as a key requirement for future Internet. In this
work we have extended the traditional AntNet routing system to
support QoS with multiple metrics such as bandwidth and delay
which is named Q-Net. This novel scalable QoS routing system aims
to provide different types of services in the network simultaneously.
Each type of service can be provided for a period of time in the
network and network nodes do not need to have any previous
knowledge about it. When a type of quality of service is requested,
Q-Net will allocate required resources for the service and will
guarantee QoS requirement of the service, based on target objectives.
Abstract: Text document categorization involves large amount
of data or features. The high dimensionality of features is a
troublesome and can affect the performance of the classification.
Therefore, feature selection is strongly considered as one of the
crucial part in text document categorization. Selecting the best
features to represent documents can reduce the dimensionality of
feature space hence increase the performance. There were many
approaches has been implemented by various researchers to
overcome this problem. This paper proposed a novel hybrid approach
for feature selection in text document categorization based on Ant
Colony Optimization (ACO) and Information Gain (IG). We also
presented state-of-the-art algorithms by several other researchers.