ANT COLONY OPTIMIZATION: SURVEY ON APPLICATIONS IN METALLURGY. Martin ČECH, Šárka VILAMOVÁ

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1 ANT COLONY OPTIMIZATION: SURVEY ON APPLICATIONS IN METALLURGY Martin ČECH, Šárka VILAMOVÁ VŠB Technical University of Ostrava, 17. listopadu 15, Ostrava, Ostrava, Czech Republic, EU, Abstract Ant colony optimization uses algorithms inspired by the foraging behaviour of real ants in the wild. Ant colony algorithms aim at finding approximate, near optimal, solutions to optimisation problems through the use of artificial ants and their communication via synthetic feromone called stigmergy. As a part of bio-inspired optimization methods are these metaheuristics generating considerable interest for solving real world problems therefore are widely investigated across many different science branches. The aim of this paper is to presents the overview of recent application in metallurgy. Other related real world applications providing inspiration for metallurgy optimization in the context with further research suggestions are also presented. Keywords: ant colony algorithm, optimization, metaheuristics 1. INTRODUCTION Biology inspired optimization methods are a category of algorithms that imitate the way nature performs. This category has been quite popular, as numerous problems can be solved without rigorous mathematical approaches. Using the properties of living systems allows us to create artificial systems while maintaining the robustness and adaptability of living systems.[1] The main reason for the intensive development of bio-inspired algorithms is the fact that these processes show a remarkable ability to find optimal or near optimal solutions even in very difficult conditions. This is suitable for solving complex optimization problems such as the optimization of empirical functions, which cannot be described mathematically, the optimization of complex sets such as sets of documents or information, or to optimize under dynamically changing conditions.[2] Bio-inspired algorithms are a proper tool for finding practically usable solution of problem in which a larger number of parameters enter, which can take a wide range of values and thus creates a wide search space for finding optimal solutions. Finding such a solution by standard methods and testing all possible solutions is too time consuming or not possible in real time. Algorithms inspired by the living nature are able to shorten the time needed to find enough good solutions and they allow us to create digital models of various logistics systems, including manufacturing, construction, supply chains or transportation [3]. This allows us to optimize various parameters of the system, such as material flow, use of resources or scheduling orders. Therefore the decision making by the bio-inspired algorithms can be applied at all levels of planning in the company. Bio-inspired algorithms can be grouped by the area of inspiration [4], as shown in Table 1 below. So far the most researched and commonly used optimization methods are algorithms inspired by evolution, which use the principle of evolution and genetics to find usable solutions. This article presents a missing overview of Ant Colony Optimization (ACO) applications in metallurgy and other related applications providing inspiration for future use of this group of algorithms at all levels of management in metallurgical companies. For that purpose mainly ProQuest and IEEE databases were

2 manually searched and suitable articles were extracted. In this article only real world metallurgy applications are being presented excluding the computational science and other research areas. Tab. 1 Various bio-inspired optimization algorithms grouped by the area of inspiration. Source: modified [a] 2. ANT COLONY ALGORITHMS Ant colony algorithms belong to the group of optimisation methods inspired by the swarm intelligence. Methods inspired by this part of biology are based on the model organism population behaviour. Swarm intelligence encompasses the implementation of collective intelligence of simple agents groups that are based on the behaviour of real world ants, as problem solving tool. The fundamental feature of ants in that colony is their social behaviour and communication with others through the environment called stigmergy as shown in Fig. 1. Fig.1 Ants stigmergy 1) the first ant find a food source (F), using some path (a), then it comes back to the nest (N), laying a pheromone trail. 2) the ants follow one of the 4 possible paths, but the reinforcement of the trail make the shortest path more appealing. 3) the ants follow the shortest path, the pheromone trail of the longest ones evaporates.

3 Real ants are capable of finding the shortest path from a food source to their nest. While walking ants deposit pheromone on the ground and follow pheromone previously deposited by other ants, the essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions.[5] In ACO, a number of artificial ants build solutions to an optimization problem and exchange information on their quality via a communication scheme that is reminiscent of the one adopted by real ants.[6] To find a shortest path, a moving ants lay some pheromone on the ground, so an ant encountering a previously trail can detect it and decide with high probability to follow it. As a result, the collective behaviour that emerges is a form of a positive feedback loop where the probability with which an ant choose a path increases with the number of ants that previously chose the same path.[7] 3. ANT COLONY OPTIMIZATION APPLICATIONS IN METALLURGY Although over four thousand articles referring to ant colony algorithms were found in the above mentioned databases, only small part of them referred to applications in metallurgy. Most of found articles deals with ant algorithm itself as computational method and refer to artificial intelligence, information technologies, programming and design of networks. When applied in metallurgy ant colony algorithms are used mostly for solving technical problems. The great example is optimization of secondary cooling in continuous casting process because of its major impact on the quality of slab. Cao, Pei and Wang [8] refer to maximum rate of cooling and surface temperature rise speed as the key factors causing inside and surface cracks of the slabs. They used ant group algorithm to optimize water distribution in the model of continuous casting secondary cooling which was established according to various metallurgical criterion like goal surface temperature, strightening spot temperature, surface temperature maximum rate of cooling, surface temperature rise speed, fluid core length etc. The secondary cooling process was also examined by Zhang [9] who developed algorithm for improving the surface temperature distribution based on ant colony algorithm through the solving of optimal secondary cooling flow rate under different casting speed. As the heat transfer coefficient in secondary cooling zone, part of the boundary conditions, is important and difficult to determine Zhenping, Biao, Zhi and Zhaoyi [10] introduced improved ant colony algorithm for parameter identification problems. This algorithm was applied to determine the convection heat transfer coefficient of secondary cooling zone for continuous billet steel caster by nail shooting and measuring the thickness of solidified shell to acquire the data. According to Song [11] the massive ore are needed in the steel-making process of the iron and steel enterprise. The blending of the ore material is the important step, which influences the following production process. XuWei [12] used ant colony algorithm to achieve burdening plan and expected ingredient. Li Zhi [13] established raw ore balanced-blending model and ant colony algorithms were used in optimization of raw ore balanced-blending for smelting iron and steel in order to settle the gradual shortage of raw ore materials. The blending proportion optimization was studied also by Hongfang [14]. The precision of SiO2 and the TFe content in blended ore was taken as the objective function. The real-time optimized computation of raw material proportioning was conducted using ant colony algorithm with good results. Ant colony algorithms are also used for solving scheduling problems as one of the main applications domains of these methods. Gravela, Priceb and Gagnéa [15] used an ant colony metaheuristics for the solution of an industrial scheduling problem in an aluminium casting centre. They present an efficient representation of a continuous horizontal casting process which takes account of a number of objectives that are important for the scheduler. Schedulers they have encountered in that project used the results produced

4 by the metaheuristic and felt that it well represents the constraints and objectives with which they were faced. ACO can be used also in terms of logistics. According to Xiao, Wang and Gui [16] train sorting and classifying operation and placing-in and taking-out operation in metallurgical industry are the key sections in operation scheduling of railway transportation. Determining the proper sorting scheme and coordinating with the placing-in and taking-out operation can reduce the time that train stays in the factory and can save the cost of the factory. Above mentioned authors used the ACO to obtain global optimal train sorting and classifying scheme and placing-in scheme after choosing the optimal taking-out schemes. 4. RELATED REAL WORLD APPLICATIONS Ant colony algorithms are widely used across many scientific branches for solving difficult combinatorial problems. The applications takes place mainly in solving scheduling problem, vehicle routing problem, traveling salesman problem, assignment problem, set problem and many others like classification, data mining, image processing, etc. Many of recent applications are providing the inspiration for future applications in metallurgy. Well researched is the vehicle routing problem with all its variances. Ant colony algorithms are used in waste management and waste routing. Bautista, Fernández and Pereira [17] used ACO for solving the urban waste collection problem represented by the model of urban waste collection area. Applications of ant heuristics can be found also in setting of railway optimal stowage. Rai-Xing and Zhen-Jiang [18] used ACO to optimize the loading capacity and volume of vehicles, besides the least number of vehicles needed in various conditions and proved used algorithm feasible and efficient. For closer view see the Railway Optimal Stowage Problem with Category Restriction. 5. CONCLUSION Although Ant Colony Optimization is getting more popular in solving complex multi-objective problems, its potential is still not fully used in metallurgy. There is a wide range of possible applications in various parts of metallurgy, including logistics, economics, manufacturing, scheduling and others. All possibilities should be examined to get answers on unexplored questions. The attention should be aimed to finding similarities between metallurgical problems and other real world cases to apply the similar approach. In recent applications of ACO and other bio-inspired optimization methods can be found great results proving that exploring such methods is reasonable and can provide high cost savings or new beneficial solutions. ACKNOWLEDGEMENT The work was supported by the specific university research of Ministry of Education, Youth and Sports of the Czech Republic No. SP2013/49. REFERENCES [1] [1] AKBARI, R., ZIARATI, K., A multilevel evolutionary algorithm for optimizing numerical functions, International Journal of Industrial Engineering Computations, 2011, Vol. 1, N. 2, p [2] [2] VALDEZ, F., MELIN, P., CASTILLO, O., Bio-Inspired Optimization Methods for Minimization of Complex Mathematical Functions, Advances in Soft Computing, 2011, Vol. 1, p [3] [3] BAUTISTA, J., PEREIRA, J., Ant Algorithms for Urban Waste Collection Routing, Lecture Notes in Computer Science: Ant Colony Optimization and Swarm Intelligence, 2004, Vol. 3172, p [4] [4] BINITHA, S., SATHYA, S.S., A Survey of Bio inspired optimization algorithms, International Journal of Soft Computing and Engineering, 2012, Vol. 2, N. 2, s

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