Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach
150
0
0.0
(
0
)
Added by
Deepyaman Maiti
Publication date
2008
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable parameter ranges for best performance of Ant System with the proposed deposition approach. Simulations reveal that the proposed deposition rule outperforms the traditional one by a large extent both in terms of solution quality and algorithm convergence. Thus, the contributions of the article can be presented as follows: i) it introduces differential equation and explores a novel method of analyzing the dynamics of ant system algorithms, ii) it initiates an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of algorithm in terms of solution quality and convergence time, iii) exhaustive experimentation performed facilitates the discovery of an algebraic relationship between the parameter set of the algorithm and feature of the problem environment.
rate research
Read More
The paper presents an exponential pheromone deposition approach to improve the performance of classical Ant System algorithm which employs uniform deposition rule. A simplified analysis using differential equations is carried out to study the stability of basic ant system dynamics with both exponential and constant deposition rules. A roadmap of connected cities, where the shortest path between two specified cities are to be found out, is taken as a platform to compare Max-Min Ant System model (an improved and popular model of Ant System algorithm) with exponential and constant deposition rules. Extensive simulations are performed to find the best parameter settings for non-uniform deposition approach and experiments with these parameter settings revealed that the above approach outstripped the traditional one by a large extent in terms of both solution quality and convergence time.
The paper presents an exponential pheromone deposition rule to modify the basic ant system algorithm which employs constant deposition rule. A stability analysis using differential equation is carried out to find out the values of parameters that make the ant system dynamics stable for both kinds of deposition rule. A roadmap of connected cities is chosen as the problem environment where the shortest route between two given cities is required to be discovered. Simulations performed with both forms of deposition approach using Elitist Ant System model reveal that the exponential deposition approach outperforms the classical one by a large extent. Exhaustive experiments are also carried out to find out the optimum setting of different controlling parameters for exponential deposition approach and an empirical relationship between the major controlling parameters of the algorithm and some features of problem environment.
We present a deterministic dynamic algorithm for maintaining a $(1+epsilon)f$-approximate minimum cost set cover with $O(flog(Cn)/epsilon^2)$ amortized update time, when the input set system is undergoing element insertions and deletions. Here, $n$ denotes the number of elements, each element appears in at most $f$ sets, and the cost of each set lies in the range $[1/C, 1]$. Our result, together with that of Gupta et al. [STOC`17], implies that there is a deterministic algorithm for this problem with $O(flog(Cn))$ amortized update time and $O(min(log n, f))$-approximation ratio, which nearly matches the polynomial-time hardness of approximation for minimum set cover in the static setting. Our update time is only $O(log (Cn))$ away from a trivial lower bound. Prior to our work, the previous best approximation ratio guaranteed by deterministic algorithms was $O(f^2)$, which was due to Bhattacharya et al. [ICALP`15]. In contrast, the only result that guaranteed $O(f)$-approximation was obtained very recently by Abboud et al. [STOC`19], who designed a dynamic algorithm with $(1+epsilon)f$-approximation ratio and $O(f^2 log n/epsilon)$ amortized update time. Besides the extra $O(f)$ factor in the update time compared to our and Gupta et al.s results, the Abboud et al. algorithm is randomized, and works only when the adversary is oblivious and the sets are unweighted (each set has the same cost). We achieve our result via the primal-dual approach, by maintaining a fractional packing solution as a dual certificate. Unlike previous primal-dual algorithms that try to satisfy some local constraints for individual sets at all time, our algorithm basically waits until the dual solution changes significantly globally, and fixes the solution only where the fix is needed.
In this paper, authors construct a new type of sequence which is named an extra-super increasing sequence, and give the definitions of the minimal super increasing sequence {a[0], a[1], ..., a[n]} and minimal extra-super increasing sequence {z[0], z[1], ..., z[n]}. Find that there always exists a fit n which makes (z[n] / z[n-1] - a[n] / a[n-1])= PHI, where PHI is the golden ratio conjugate with a finite precision in the range of computer expression. Further, derive the formula radic(5) = 2(z[n] / z[n-1] - a[n] / a[n-1]) + 1, where n corresponds to the demanded precision. Experiments demonstrate that the approach to radic(5) through a term ratio difference is more smooth and expeditious than through a Taylor power series, and convince the authors that lim(n to infinity) (z[n] / z[n-1] - a[n] / a[n-1]) = PHI holds.
Modern scientific research has become largely a cooperative activity in the Internet age. We build a simulation model to understand the population-level creativity based on the heuristic ant colony algorithm. Each researcher has two heuristic parameters characterizing the goodness of his own judgments and his trust on literature. In a population with all kinds of researchers, we find that as the problem scale increases, the contributor distribution significantly shifts from the independent regime of relying on ones own judgments to the cooperative regime of more closely following the literature. The distribution also changes with the stage of the research problem and the computing power available. Our work provides some preliminary understanding and guidance for the dynamical process of cooperative scientific research in various disciplines.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
