scholarly journals Ant colony optimization for feasible scheduling of step-controlled smart grid generation

2021 ◽  
Author(s):  
Jörg Bremer ◽  
Sebastian Lehnhoff
Keyword(s):  
Ant Colony Optimization ◽  
Combinatorial Problem ◽  
Electrical Energy ◽  
Intermediate Energy ◽  
Ant Colony ◽  
Energy Resources ◽  
Artificial Ants ◽  
Decentralized Energy ◽  
Feasible Solutions ◽  
Energy Buffer

AbstractThe electrical energy grid is currently experiencing a paradigm shift in control. In the future, small and decentralized energy resources will have to responsibly perform control tasks like frequency or voltage control. For many use cases, scheduling of energy resources is necessary. In the multi-dimensional discrete case–e.g.,  for step-controlled devices–this is an NP-hard problem if some sort of intermediate energy buffer is involved. Systematically constructing feasible solutions during optimization, hence, becomes a difficult task. We prove the NP-hardness for the example of co-generation plants and demonstrate the multi-modality of systematically designing feasible solutions. For the example of day-ahead scheduling, a model-integrated solution based on ant colony optimization has already been proposed. By using a simulation model for deciding on feasible branches, artificial ants construct the feasible search graphs on demand. Thus, the exponential growth of the graph in this combinatorial problem is avoided. We present in this extended work additional insight into the complexity and structure of the underlying the feasibility landscape and additional simulation results.

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.

Information Hiding Using Ant Colony Optimization Algorithm

2011 ◽  
Vol 2 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Wasan Shaker Awad
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Information Hiding ◽  
Ant Colony ◽  
Secret Message ◽  
Least Mean Square ◽  
Ant Colony Optimization Algorithm ◽  
Efficient Information ◽  
Feasible Solutions ◽  
Optimal Set

This paper aims to find an effective and efficient information hiding method used for protecting secret information by embedding it in a cover media such as images. Finding the optimal set of the image pixel bits to be substituted by the secret message bits, such that the cover image is of high quality, is a complex process and there is an exponential number of feasible solutions. Two new ant-based algorithms are proposed and compared with other algorithms. The experimental results show that ant colony optimization algorithm can find the solution efficiently and effectively by finding the optimal set of pixel bits in a few number of iterations and with least Mean Square Error (MSE) comparable with genetic and genetic simulated annealing algorithms.

Information Hiding Using Ant Colony Optimization Algorithm

2013 ◽  
pp. 289-301 ◽  
Author(s):  
Wasan Shaker Awad
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Information Hiding ◽  
Ant Colony ◽  
Secret Message ◽  
Least Mean Square ◽  
Ant Colony Optimization Algorithm ◽  
Complex Process ◽  
Feasible Solutions ◽  
Optimal Set

This paper aims to find an effective and efficient information hiding method used for protecting secret information by embedding it in a cover media such as images. Finding the optimal set of the image pixel bits to be substituted by the secret message bits, such that the cover image is of high quality, is a complex process and there is an exponential number of feasible solutions. Two new ant-based algorithms are proposed and compared with other algorithms. The experimental results show that ant colony optimization algorithm can find the solution efficiently and effectively by finding the optimal set of pixel bits in a few number of iterations and with least Mean Square Error (MSE) comparable with genetic and genetic simulated annealing algorithms.

scholarly journals Finding the shortest path using the ant colony optimization

2017 ◽  
Vol 7 (1.1) ◽  
pp. 392
Author(s):  
K Yella Swamy ◽  
Saranya Gogineni ◽  
Yaswanth Gunturu ◽  
Deepchand Gudapati ◽  
Ramu Tirumalasetti
Keyword(s):  
Ant Colony Optimization ◽  
Optimal Solution ◽  
Ant Colony ◽  
Main Concept ◽  
Pheromone Trails ◽  
Artificial Ants ◽  
Computer Based ◽  
Hard Problems ◽  
Short Time ◽  
Np Hard Problems

An ant colony optimization(ACO) is a techniquewhich is recently introduced ,and it is applied to solve several np-hard problems ,we can get optimal solution in a short time Main concept of the ACO is based on the behavior of ants in their colony for finding a source of food. They will communicate indirectly through pheromone trails. Computer based simulation is can generate good solution by using artificial ants, by using that general behavior we are solving travelling Sale man problem.

Feature Selection Based on Ant Colony Optimization for Image Classification

2013 ◽  
Vol 319 ◽  
pp. 337-342
Author(s):  
Li Tu ◽  
Li Zhi Yang
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Image Data ◽  
Ant Colony ◽  
Data Sets ◽  
Classification Rules ◽  
Selection Algorithm ◽  
Artificial Ants

In this paper, a feature selection algorithm based on ant colony optimization (ACO) is presented to construct classification rules for image classification. Most existing ACO-based algorithms use the graph with O(n2) edges. In contrast, the artificial ants in the proposed algorithm FSC-ACO traverse on a feature graph with only O(n) edges. During the process of feature selection, ants construct the classification rules for each class according to the improved pheromone and heuristic functions. FSC-ACO improves the qualities of rules depend on the classification accuracy and the length of rules. The experimental results on both standard and real image data sets show that the proposed algorithm can outperform the other related methods with fewer features in terms of speed, recall and classification accuracy.

scholarly journals Minimum Cost Multicast Routing Using Ant Colony Optimization Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xiao-Min Hu ◽  
Jun Zhang
Keyword(s):  
Ant Colony Optimization ◽  
Multicast Routing ◽  
Minimum Cost ◽  
Multicast Tree ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Specific Knowledge ◽  
Artificial Ants ◽  
Multicast Trees ◽  
Pheromone Information

Multicast routing (MR) is a technology for delivering network data from some source node(s) to a group of destination nodes. The objective of the minimum cost MR (MCMR) problem is to find an optimal multicast tree with the minimum cost for MR. This problem is NP complete. In order to tackle the problem, this paper proposes a novel algorithm termed the minimum cost multicast routing ant colony optimization (MCMRACO). Based on the ant colony optimization (ACO) framework, the artificial ants in the proposed algorithm use a probabilistic greedy realization of Prim’s algorithm to construct multicast trees. Moving in a cost complete graph (CCG) of the network topology, the ants build solutions according to the heuristic and pheromone information. The heuristic information represents problem-specific knowledge for the ants to construct solutions. The pheromone update mechanisms coordinate the ants’ activities by modulating the pheromones. The algorithm can quickly respond to the changes of multicast nodes in a dynamic MR environment. The performance of the proposed algorithm has been compared with published results available in the literature. Results show that the proposed algorithm performs well in both static and dynamic MCMR problems.

scholarly journals Extension of the constrained ant colony optimization algorithms for the optimal operation of multi-reservoir systems

2012 ◽  
Vol 15 (1) ◽  
pp. 155-173 ◽  
Author(s):  
R. Moeini ◽  
M. H. Afshar
Keyword(s):  
Ant Colony Optimization ◽  
Reservoir Operation ◽  
Large Scale ◽  
Optimization Algorithms ◽  
Optimal Operation ◽  
Ant Colony ◽  
Ant System ◽  
Reservoir Systems ◽  
Feasible Solutions ◽  
Ant Colony Optimization Algorithms

This paper extends the application of Constrained Ant Colony Optimization Algorithms (CACOAs) to optimal operation of multi-reservoir systems. Three different formulations of the constrained Ant Colony Optimization (ACO) are outlined here using Max-Min Ant System for the solution of multi-reservoir operation problems. In the first two versions, called Partially Constrained ACO algorithms, the constraints of the multi-reservoir operation problems are satisfied partially. In the third formulation, all the constraints of the underlying problem are implicitly satisfied by the provision of tabu lists to the ants which contain only feasible options. The ants are, therefore, forced to construct feasible solutions and hence the method is referred to as a Fully Constrained ACO algorithm. The proposed constrained ACO algorithms are formulated for both possible cases of taking storage/release volumes as the decision variables of the problem. The proposed methods are used to optimally solve the well-known problems of four- and ten-reservoir operations and the results are presented and compared with those of the conventional unconstrained ACO algorithm and existing methods in the literature. The results indicate the superiority of the proposed methods over conventional ACOs and existing methods to optimally solve large scale multi-reservoir operation problems.

Modeling the Dynamics of Ant Colony Optimization

2002 ◽  
Vol 10 (3) ◽  
pp. 235-262 ◽  
Author(s):  
Daniel Merkle ◽  
Martin Middendorf
Keyword(s):  
Ant Colony Optimization ◽  
Dynamic Behavior ◽  
Deterministic Model ◽  
Ant Colony ◽  
Iterative Approach ◽  
Complex Dynamic ◽  
Artificial Ants ◽  
Permutation Problems ◽  
Pheromone Information

The dynamics of Ant Colony Optimization (ACO) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The ACO optimization metaheuristic is an iterative approach, where in every iteration, artificial ants construct solutions randomly but guided by pheromone information stemming from former ants that found good solutions. The behavior of ACO algorithms and the ACO model are analyzed for certain types of permutation problems. It is shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the pheromone matrix. This explains why ACO algorithms can show a complex dynamic behavior even when there is only one ant per iteration and no competition occurs. The ACO model is used to describe the algorithm behavior as a combination of situations with different degrees of competition between the ants. This helps to better understand the dynamics of the algorithm when there are several ants per iteration as is always the case when using ACO algorithms for optimization. Simulations are done to compare the behavior of the ACO model with the ACO algorithm. Results show that the deterministic model describes essential features of the dynamics of ACO algorithms quite accurately, while other aspects of the algorithms behavior cannot be found in the model.

scholarly journals Segmentation and Edge Detection Based on Modified ant Colony Optimization for Iris Image Processing

2013 ◽  
Vol 3 (2) ◽  
pp. 133-141 ◽  
Author(s):  
Abbas Biniaz ◽  
Ataollah Abbasi
Keyword(s):  
Image Processing ◽  
Ant Colony Optimization ◽  
Iris Recognition ◽  
Ant Colony ◽  
Iteration Step ◽  
Complex Structures ◽  
Local Minima ◽  
Iris Segmentation ◽  
Artificial Ants ◽  
Traditional Approaches

Abstract Ant colony optimization (stocktickerACO) is a meta-heuristic algorithm inspired by food searching behavior of real ants. Recently stocktickerACO has been widely used in digital image processing. When artificial ants move in a discrete habitat like an image, they deposit pheromone in their prior position. Simultaneously, vaporizing of pheromone in each iteration step avoids from falling in the local minima trap. Iris recognition because of its great dependability and non-invasion has various applications. simulation results demonstrate stocktickerACO algorithm can effectively extract the iris texture. Also it is not sensitive to nuisance factors. Moreover, stocktickerACO in this research preserves details of the various synthetic and real images. Performance of ACO in iris segmentation is compared with operation of traditional approaches such as canny, robert, and sobel edge detections. Experimental results reveal high quality and quite promising of stocktickerACO to segment images with irregular and complex structures.

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