Ant colony optimization for dynamic combinatorial optimization problems

2018 ◽  
pp. 121-142
Author(s):  
Michalis Mavrovouniotis ◽  
Shengxiang Yang
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Ant Colony ◽  
Combinatorial Optimization Problems

scholarly journals Enhanced Hyper-Cube Framework Ant Colony Optimization for Combinatorial Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 286
Author(s):  
Ali Ahmid ◽  
Thien-My Dao ◽  
Ngan Van Le
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
The Right ◽  
Design Cost ◽  
Laminated Structures

Solving of combinatorial optimization problems is a common practice in real-life engineering applications. Trusses, cranes, and composite laminated structures are some good examples that fall under this category of optimization problems. Those examples have a common feature of discrete design domain that turn them into a set of NP-hard optimization problems. Determining the right optimization algorithm for such problems is a precious point that tends to impact the overall cost of the design process. Furthermore, reinforcing the performance of a prospective optimization algorithm reduces the design cost. In the current study, a comprehensive assessment criterion has been developed to assess the performance of meta-heuristic (MH) solutions in the domain of structural design. Thereafter, the proposed criterion was employed to compare five different variants of Ant Colony Optimization (ACO). It was done by using a well-known structural optimization problem of laminate Stacking Sequence Design (SSD). The initial results of the comparison study reveal that the Hyper-Cube Framework (HCF) ACO variant outperforms the others. Consequently, an investigation of further improvement led to introducing an enhanced version of HCFACO (or EHCFACO). Eventually, the performance assessment of the EHCFACO variant showed that the average practical reliability became more than twice that of the standard ACO, and the normalized price decreased more to hold at 28.92 instead of 51.17.

scholarly journals A Multiple Pheromone Table Based Ant Colony Optimization for Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Kai-Cheng Hu ◽  
Chun-Wei Tsai ◽  
Ming-Chao Chiang ◽  
Chu-Sing Yang
Keyword(s):  
Approximate Solution ◽  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
High Probability ◽  
Search Strategy ◽  
Optimization Problems ◽  
Clustering Algorithms ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
Search Directions

Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the “original” pheromone table used to keep track of thepromisinginformation, a second pheromone table is added to the proposed algorithm to keep track of theunpromisinginformation so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.

Ant Colony Optimization Algorithm in Intrusion Detection and Positive

2013 ◽  
Vol 443 ◽  
pp. 541-545
Author(s):  
Qian Zou ◽  
Hua Jun Wang ◽  
Wei Huang ◽  
Jin Pan
Keyword(s):  
Combinatorial Optimization ◽  
Intrusion Detection ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Ant Colony ◽  
Effective Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Combinatorial Optimization Problems

Ant colony algorithm is an effective algorithm to solve combinatorial optimization problems, it has many good features, and there are also some disadvantages. In this paper, through research on ant colony optimization algorithm, apply it in intrusion detection. Then it gives an improved ant colony optimization algorithm. Tests show that the algorithm improves the efficiency of intrusion detection, reduces false positives of intrusion detection.

scholarly journals Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 361
Author(s):  
Teddy Nurcahyadi ◽  
Christian Blum
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Dominating Set ◽  
Ant Colony ◽  
Alternative Mechanism ◽  
Learning Approaches ◽  
Learning Mechanisms ◽  
Combinatorial Optimization Problems ◽  
Dominating Set Problem

Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. This is also the case in other learning-based metaheuristics such as evolutionary algorithms and particle swarm optimization. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this work we present and study an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. Moreover, we compare our proposal to some well-known existing negative learning approaches from the related literature. Our study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases we are able to show that our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

Ant Colony Optimization of Multilayer Shielding for Mixed Neutron and Gamma Radiations: A Preliminary Study

2017 ◽  
Author(s):  
Muhammad Arif Bin Sazali ◽  
Nahrul Khair Alang Md Rashid ◽  
Khaidzir Hamzah
Keyword(s):  
Ant Colony Optimization ◽  
Gamma Rays ◽  
Radiation Spectrum ◽  
Optimization Problems ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
Final Layer ◽  
Preliminary Study ◽  
Gamma Radiations ◽  
Combination Of Material

Mixed neutron and gamma radiations require different shielding materials as their interaction with materials is different. Composites were developed in order to combine the shielding capabilities of different materials. However, their homogeneity is difficult to be assured which can lead to pinholes where radiation can penetrate. To avoid this problem, several materials arranged in layers can be used to shield against mixed radiations. Since the multilayer shielding can be made from any material in many configurations, the ant colony optimization (ACO) is a promising method because it deals with combinatorial optimization problems. The candidate materials are HDPE, boron, cadmium, gadolinium, tungsten, bismuth, and iron. Preliminary MCNP simulations were done to observe the effect of arrangements, thicknesses, and types of materials on the radiation spectrum. It was found that: (1) the final layer should be made of high density material, (2) an increase beyond certain thicknesses did not result in a significant increase in attenuation, and (3) there should be an optimum combination of material that can effectively shield against both neutrons and gamma rays.

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