# Applied Intelligence Journal Impact Factor & Information

Publisher: Springer Science+Business Media, Springer Verlag

## Journal description

The international journal of Applied Intelligence provides a medium for exchanging scientific research and technological achievements accomplished by the international community. The focus of the work is on research in artificial intelligence and neural networks. The journal addresses issues involving solutions of real-life manufacturing defense management government and industrial problems which are too complex to be solved through conventional approaches and which require the simulation of intelligent thought processes heuristics applications of knowledge and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The emphasis of the reported work is on new and original research and technological developments rather than reports on the application of existing technology to different sets of data. Earlier work reported in these fields has been limited in application and has solved simplified structured problems which rarely occur in real-life situations. Only recently have researchers started addressing real and complex issues applicable to difficult problems. The journal welcomes such developments and functions as a catalyst in disseminating the original research and technological achievements of the international community in these areas.

## Impact Factor Rankings

2015 Impact Factor Available summer 2015 1.853 0.849 0.881 0.988 0.775 0.5 0.329 0.569 0.477 0.776 0.686 0.493 0.42 0.291 0.326 0.268 0.139 0.05

## Impact factor over time

Impact factor
.
Year

5-year impact 1.94 5.90 0.19 0.00 0.30 Applied Intelligence website Applied intelligence (Dordrecht, Netherlands) 0924-669X 25272842 Periodical, Internet resource Journal / Magazine / Newspaper, Internet Resource

## Publisher details

• Pre-print
• Author can archive a pre-print version
• Post-print
• Author can archive a post-print version
• Conditions
• Author's pre-print on pre-print servers such as arXiv.org
• Author's post-print on author's personal website immediately
• Author's post-print on any open access repository after 12 months after publication
• Publisher's version/PDF cannot be used
• Published source must be acknowledged
• Must link to publisher version
• Set phrase to accompany link to published version (see policy)
• Articles in some journals can be made Open Access on payment of additional charge
• Classification
​ green

## Publications in this journal

• ##### Article: Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population
Arie Wahyu, Ayu Purwarianti, Le Hoang Son
[Hide abstract]
ABSTRACT: Geo-demographic analysis is an essential part of a geographical information system (GIS) for predicting people’s behavior based on statistical models and their residential location. Fuzzy Geographically Weighted Clustering (FGWC) serves as one of the most efficient algorithms in geo-demographic analysis. Despite being an effective algorithm, FGWC is sensitive to initialize when the random selection of cluster centers makes the iterative process falling into the local optimal solution easily. Artificial Bee Colony (ABC), one of the most popular meta-heuristic algorithms, can be regarded as the tool to achieve global optimization solutions. This research aims to propose a novel geo-demographic analysis algorithm that integrates FGWC to the optimization scheme of ABC for improving geo-demographic clustering accuracy. Experimental results on various datasets show that the clustering quality of the proposed algorithm called FGWC-ABC is better than those of other relevant methods. The proposed algorithm is also applied to a decision-making application for analyzing crime behavior problem in the population using the US communities and crime data set. It provides fuzzy rules to determine the violent crime rate in terms of linguistic labels from socioeconomic variables. These results are significant to make predictions of further US violent crime rate and to facilitate appropriate decisions on prevention such the situations in the future.
Applied Intelligence 09/2015; 43(2).
• ##### Article: Integration of improved predictive model and adaptive differential evolution based dynamic multi-objective evolutionary optimization algorithm
[Hide abstract]
ABSTRACT: A novel dynamic multi-objective optimization evolutionary algorithm is proposed in this paper to track the Pareto-optimal set of time-changing multi-objective optimization problems. In the proposed algorithm, to initialize the new population when a change is detected, a modified prediction model utilizng the historical optimal sets obtained in the last two times is adopted. Meantime, to improve both convergence and diversity, a self-adaptive differential evolution crossover operator is used. We conducted two experiments: the first one compares the proposed algorithm with the other three dynamic multiobjective evolutionary algorithms, and the second one investigates the performance of the two proposed operators. The statistical results indicate that the proposed algorithm has better conergence speed and diversity and it is very promising for dealing with dynamic environment.
Applied Intelligence 07/2015; 43(1). DOI:10.1007/s10489-014-0625-y
• ##### Article: A cooperative coevolutionary biogeography-based optimizer
[Hide abstract]
ABSTRACT: With its unique migration operator and mutation operator, Biogeography-Based Optimization (BBO), which simulates migration of species in natural biogeography, is different from existing evolutionary algorithms, but it has shortcomings such as poor convergence precision and slow convergence speed when it is applied to solve complex optimization problems. Therefore, we put forward a Cooperative Coevolutionary Biogeography-Based Optimizer (CBBO) in this paper. In CBBO, the whole population is divided into multiple sub-populations first, and then each subpopulation is evolved with an improved BBO separately. The fitness evaluation of habitats of a subpopulation is conducted by constructing context vectors with selected habitats from other sub-populations. Our CBBO tests are based on 13 benchmark functions and are also compared with several other evolutionary algorithms. Experimental results demonstrate that CBBO is able to achieve better results than other evolutionary algorithms on most of the benchmark functions.
Applied Intelligence 07/2015; 43(1). DOI:10.1007/s10489-014-0627-9
• ##### Article: A time series retrieval tool for sub-series matching
[Hide abstract]
ABSTRACT: The problem of retrieving time series similar to a specified query pattern has been recently addressed within the case based reasoning (CBR) literature. Providing a flexible and efficient way of dealing with such an issue is of paramount importance in many domains (e.g., medical), where the evolution of specific parameters is collected in the form of time series. In the past, we have developed a framework for retrieving time series, applying temporal abstractions. With respect to more classical (mathematical) approaches, our framework provides significant advantages. In particular, multi-level abstraction mechanisms and proper indexing techniques allow for flexible query issuing, and for efficient and interactive query answering. In this paper, we present an extension to such a framework, which aims to support sub-series matching as well. Indeed, sub-series retrieval may be crucial when the whole time series evolution is not of interest, while critical patterns to be searched for are only “local”. Moreover, sometimes the relative order of patterns, but not their precise location in time, may be known. Finally, an interactive search, at different abstraction levels, may be required by the decision maker. Our extended framework (which is currently being applied in haemodialysis, but is domain independent) deals with all these issues.
Applied Intelligence 07/2015; 43(1). DOI:10.1007/s10489-014-0628-8
• ##### Article: EIFDD: An efficient approach for erasable itemset mining of very dense datasets
[Hide abstract]
ABSTRACT: Erasable itemset mining, first proposed in 2009, is an interesting problem in supply chain optimization. The dPidset structure, a very effective structure for mining erasable itemsets, was introduced in 2014. The dPidset structure outperforms previous structures such as PID_List and NC_Set. Algorithms based on dPidset can effectively mine erasable itemsets. However, for very dense datasets, the mining time and memory usage are large. Therefore, this paper proposes an effective approach that uses the subsume concept for mining erasable itemsets for very dense datasets. The subsume concept is used to help early determine the information of a large number of erasable itemsets without the usual computational cost. Then, the erasable itemsets for very dense datasets (EIFDD) algorithm, which uses the subsume concept and the dPidset structure for the erasable itemset mining of very dense datasets, is proposed. An illustrative example is given to demonstrate the proposed algorithm. Finally, an experiment is conducted to show the effectiveness of EIFDD.
Applied Intelligence 07/2015; 43(1). DOI:10.1007/s10489-014-0644-8
• ##### Article: A novel approach to task assignment in a cooperative multi-agent design system
[Hide abstract]
ABSTRACT: The task assignment problem is an important topic in multi-agent systems research. Distributed real-time systems must accommodate a number of communication tasks, and the difficulty in building such systems lies in task assignment (i.e., where to place the tasks). This paper presents a novel approach that is based on artificial bee colony algorithm (ABC) to address dynamic task assignment problems in multi-agent cooperative systems. The initial bee population (solution) is constructed by the initial task assignment algorithm through a greedy heuristic. Each bee is formed by the number of tasks and agents, and the number of employed bees is equal to the number of onlooker bees. After being generated, the solution is improved through a local search process called greedy selection. This process is implemented by onlooker and employed bees. In greedy selection, if the fitness value of the candidate source is greater than that of the current source, the bee forgets the current source and memorizes the new candidate source. Experiments are performed with two test suites (TIG representing real-life tree and Fork–Join problems and randomly generated TIGs). Results are compared with other nature-inspired approaches, such as genetic and particle swarm optimization algorithms, in terms of CPU time and communication cost. The findings show that ABC improves these two criteria significantly with respect to the other approaches.
Applied Intelligence 07/2015; 43(1). DOI:10.1007/s10489-014-0640-z
• ##### Article: A mnemonic shuffled frog leaping algorithm with cooperation and mutation
[Hide abstract]
ABSTRACT: Shuffled frog leaping algorithm (SFLA) has shown its good performance in many optimization problems. This paper proposes a Mnemonic Shuffled Frog Leaping Algorithm with Cooperation and Mutation (MSFLACM), which is inspired by the competition and cooperation methods of different evolutionary computing, such as PSO, GA, and etc. In the algorithm, shuffled frog leaping algorithm and improved local search strategy, cooperation and mutation to improve accuracy and that exhibits strong robustness and high accuracy for high-dimensional continuous function optimization. A modified shuffled frog leaping algorithm (MSFLA) is investigated that improves the leaping rule by combining velocity updating equation of PSO. To improve accuracy, if the worst position in the memeplex couldn’t get a better position in the local exploration procedure of the MSFLA, the paper introduces cooperation and mutation, which prevents local optimum and updates the worst position in the memeplex. By making comparative experiments on several widely used benchmark functions, analysis results show that the performances of that improved variant are more promising than the recently developed SFLA for searching optimum value of unimodal or multimodal continuous functions.
Applied Intelligence 07/2015; 43(1). DOI:10.1007/s10489-014-0642-x
• ##### Article: How effective is the Grey Wolf optimizer in training multi-layer perceptrons
[Hide abstract]
ABSTRACT: This paper employs the recently proposed Grey Wolf Optimizer (GWO) for training Multi-Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification and three function-approximation datasets are utilized to benchmark the performance of the proposed method. For verification, the results are compared with some of the most well-known evolutionary trainers: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolution Strategy (ES), and Population-based Incremental Learning (PBIL). The statistical results prove the GWO algorithm is able to provide very competitive results in terms of improved local optima avoidance. The results also demonstrate a high level of accuracy in classification and approximation of the proposed trainer.
Applied Intelligence 07/2015; 43(1). DOI:10.1007/s10489-014-0645-7
• ##### Article: Mining frequent closed inter-sequence patterns efficiently using dynamic bit vectors
[Hide abstract]
ABSTRACT: Mining frequent sequences is a critical stage before rule generation for sequence databases. Currently, there are two main ways for mining frequent sequences, namely intra-sequence mining and inter-sequence mining. Inter-sequence mining is more attractive than intra-sequence mining because it considers the relationship between sequences in transactions. However, mining all possible frequent inter-sequences takes a long time and requires a lot of memory. Mining frequent closed inter-sequences is efficient because such sequences are compact, and only the necessary information is maintained. CISP-Miner was proposed for mining frequent closed inter-sequence patterns, but it consumes a lot of memory since many closed patterns are mined. This paper proposes an algorithm called ClosedISP for mining frequent closed inter-sequence patterns. The proposed algorithm uses a checking scheme for early eliminating and checking closed patterns without candidate maintenance. ClosedISP uses a dynamic bit vector that combines transaction information to compress data. In addition, ClosedISP adopts a prefix tree and a depth-first search order to reduce the search space and generate non-redundant sequential rules efficiently. Experiments were conducted to compare the proposed algorithm with CISP-Miner to demonstrate the effectiveness of the proposed algorithm in terms of runtime and memory usage.
Applied Intelligence 07/2015; 43(1). DOI:10.1007/s10489-014-0630-1
• ##### Article: Prediction intervals in supervised learning for model evaluation and discrimination
[Hide abstract]
ABSTRACT: In this paper we explore prediction intervals and how they can be used for model evaluation and discrimination in the supervised regression setting of medium sized datasets. We review three different methods for making prediction intervals and the statistics used for their evaluation. How the prediction intervals look like, how different methods behave and how the prediction intervals can be utilized for the graphical evaluation of models is illustrated with the help of simple datasets. Afterwards we propose a combined method for making prediction intervals and explore its performance with two voting schemes for combining predictions of a diverse ensemble of models. All methods are tested on a large set of datasets on which we evaluate individual methods and aggregated variants for their abilities of selecting the best predictions. The analysis of correlations between the root mean squared error and our evaluation statistic show that both stability and reliability of the results increase as the techniques get more elaborate. We confirm that the methodology is suitable for the graphical comparison of individual models and is a viable way of discriminating among model candidates.
Applied Intelligence 06/2015; 42(4). DOI:10.1007/s10489-014-0632-z
• ##### Article: Link prediction in dynamic social networks by integrating different types of information
[Hide abstract]
Applied Intelligence 06/2015; 42(4). DOI:10.1007/s10489-014-0631-0
• ##### Article: Best-order crossover for permutation-based evolutionary algorithms
[Hide abstract]
ABSTRACT: Permutation-based encoding is used by many evolutionary algorithms dealing with combinatorial optimization problems. An important aspect of the evolutionary search process refers to the recombination process of existing individuals in order to generate new potentially better fit offspring leading to more promising areas of the search space. In this paper, we describe and analyze the best-order recombination operator for permutation-based encoding. The proposed operator uses genetic information from the two parents and from the best individual obtained up to the current generation. These sources of information are integrated to determine the best order of values in the new permutation. In order to evaluate the performance of best-order crossover, we address three well-known $$\mathcal {NP}$$ -hard optimization problems i.e. Travelling Salesman Problem, Vehicle Routing Problem and Resource-Constrained Project Scheduling Problem. For each of these problems, a set of benchmark instances is considered in a comparative analysis of the proposed operator with eight other crossover schemes designed for permutation representation. All crossover operators are integrated in the same standard evolutionary framework and using the same parameter setting to allow a comparison focused on the recombination process. Numerical results emphasize a good performance of the proposed crossover scheme which is able to lead to overall better quality solutions.
Applied Intelligence 06/2015; 42(4). DOI:10.1007/s10489-014-0623-0
• ##### Article: Computing contingency tables from sparse ADtrees
[Hide abstract]
ABSTRACT: In data-mining algorithms contingency tables are frequently built from ADtrees, as ADtrees have been demonstrated to be an efficient data structure for caching sufficient statistics. This paper introduces three modifications. The first two use a one-dimensional array and a hash map for representing contingency tables, and the third uses the non-recursive approach to build contingency tables from sparse ADtrees. We implement algorithms to construct contingency tables with a two-dimensional array, a tree, a one-dimensional array, and a hash map using recursion and non-recursive approaches in Python. We empirically test these algorithms in five aspects with a large number of randomly generated datasets. We also apply the modified algorithms to Bayesian networks learning and test the performance improvements using three real-life datasets. We demonstrate experimentally that all three of these modifications improve algorithm performance. The improvements are more significant with higher arities and larger arity values.
Applied Intelligence 06/2015; 42(4). DOI:10.1007/s10489-014-0624-z