Heuristics and Metaheuristics Algorithms
Mehdi Khadem; Abbas Toloie Eshlaghy; kiamars Fathi Hafshejani
Abstract
Over the past decade, solving complex optimization problems with metaheuristic algorithms has attracted many experts and researchers.There are exact methods and approximate methods to solve optimization problems. Nature has always been a model for humans to draw the best mechanisms and the best engineering ...
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Over the past decade, solving complex optimization problems with metaheuristic algorithms has attracted many experts and researchers.There are exact methods and approximate methods to solve optimization problems. Nature has always been a model for humans to draw the best mechanisms and the best engineering out of it and use it to solve their problems. The concept of optimization is evident in several natural processes, such as the evolution of species, the behavior of social groups, the immune system, and the search strategies of various animal populations. For this purpose, the use of nature-inspired optimization algorithms is increasingly being developed to solve various scientific and engineering problems due to their simplicity and flexibility. Anything in a particular situation can solve a significant problem for human society. This paper presents a comprehensive overview of the metaheuristic algorithms and classifications in this field and offers a novel classification based on the features of these algorithms.
Heuristics and Metaheuristics Algorithms
Ali Sanagooy Aghdam; Mohammad Ali Afshar Kazemi; Abbas Toloie Eshlaghy
Abstract
Optimized asset tracking with Radio Frequency Identification (RFID) as a complicated innovation that requires much money to be implemented has become more popular in the healthcare industry. Considering the use of more antennas in each reader, we present a modern heuristic methods, hybrid of Genetic ...
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Optimized asset tracking with Radio Frequency Identification (RFID) as a complicated innovation that requires much money to be implemented has become more popular in the healthcare industry. Considering the use of more antennas in each reader, we present a modern heuristic methods, hybrid of Genetic Algorithms (GA) and Simulated Annealing (SA) for the purpose of placing readers in an emergency department of a hospital with an RFID network. In this study, a multi-objective function is developed for the network coverage maximization and the minimization of total cost, tag reader collision, interference, energy consumption, and path loss in a simultaneous way. The proposed algorithm provides savings (on average) in the total cost of the RFID network through the efficient use of three types of readers with one, two and four antenna ports. Additionally, by testing three scenarios, the effect of algorithms in achieving the optimal solution is indicated by the simulated results. Besides, the results of GA-SA is compared to the results of GA and other existing models in the relevant literature. It is shown that its main advantage is the use of multi-antenna RFID readers, which reduces the total cost of the RFID network and also increase network coverage with fewer readers and antennas. In other words, contributions for the research are proposing a hybrid GA-SA algorithm, developing a multi-objective function, testing the algorithm in a hospital setting, and comparing the results of GA-SA with GA.
Heuristics and Metaheuristics Algorithms
Hojatollah Rajabi Moshtaghi; Abbas Toloie Eshlaghy; Mohammad Reza Motadel
Abstract
Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various fields. ...
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Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various fields. These algorithms, in contrast to exact optimization methods, find the solutions which are very close to the global optimum solution as possible, in such a way that this solution satisfies the threshold constraint with an acceptable level. Most of the meta-heuristic algorithms are inspired by natural phenomena. In this research, a comprehensive review on meta-heuristic algorithms is presented to introduce a large number of them (i.e. about 110 algorithms). Moreover, this research provides a brief explanation along with the source of their inspiration for each algorithm. Also, these algorithms are categorized based on the type of algorithms (e.g. swarm-based, evolutionary, physics-based, and human-based), nature-inspired vs non-nature-inspired based, population-based vs single-solution based. Finally, we present a novel classification of meta-heuristic algorithms based on the country of origin.
Heuristics and Metaheuristics Algorithms
Hadi Roshan; Masoumeh Afsharinezhad
Abstract
Data analytics allows companies mining the patterns and trends in their customers data to implement more effective market segmentation strategies, then customize promotional offers, allocate marketing resources efficiently, and improve customer relationship management. However the implementation of such ...
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Data analytics allows companies mining the patterns and trends in their customers data to implement more effective market segmentation strategies, then customize promotional offers, allocate marketing resources efficiently, and improve customer relationship management. However the implementation of such strategies often hampered by limited budgets and the ever-changing priorities and goals of marketing campaigns. So, This paper suggests and demonstrates the novel approach dividing a broad target market into subsets of consumers who have common needs, interests, and priorities, and then designing and implementing strategies to target them to achieve profit maximization. Therefore, the aims of this study are twofold, first, is to use historical data (such as purchased items and the associative monetary expenses), the proposed model identifies customer segments based on Firefly Algorithm (FA). Second, is the identification of the most profitable segment according to the RFM model (recency, frequency and monetary). In this article real marketing data are used to illustrate the proposed approach.
