Forthcoming articles


International Journal of Swarm Intelligence


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International Journal of Swarm Intelligence (4 papers in press)


Regular Issues


  • Concurrent parametric optimization of single pass end milling through GRA coupled with PSO for calmax-635 die steel   Order a copy of this article
    by Bikash Bepari, Ankit  
    Abstract: The present experimental investigation is to obtain optimized parametric combination for enhanced surface quality during finishing of Calmax-635 die steel through Grey Relational Analysis (GRA) coupled with Particle Swarm Optimization (PSO). GRA converts multiple objectives into a single objective by defining the Grey Relational Grade (GRG). However, it yields discrete parametric combination within the problem space and fetches quasi-optimal solution. Whereas, PSO being a swarm intelligent technique can obtain optimal solution if the fitness function is available. Due to absence of the fitness function for Calmax-635 die steel in the literature, a full factorial Design of Experiment (DOE) was conducted for milling parameters like, spindle speed, feed rate and depth of cut all at three levels. With the help of regression analysis and ANOVA, a relationship between the process parameters and GRG was established which acted as a fitness function for PSO within the problem space. Thus, when PSO was coupled with GRA, the optimized process parameters became 5660.6 rpm, 579.4 mm/min, 0.105 mm respectively and the roughness values obtained were 0.862 m, 6.591 m and 4.638 m for Ra, Rmax and Rz respectively which showed significant improvement pertinent to surface quality. Therefore the proposed methodology reveals an alternative way for optimization in absentia of fitness function.
    Keywords: Calmax-635; Grey relational analysis; Principal component analysis; Grey relation grade; Particle Swarm Optimization; Surface quality.

  • Improving The Cooperation of Fuzzy Simplified Memory A* Search and Particle Swarm Optimization for path planning   Order a copy of this article
    by Mehdi Neshat 
    Abstract: Problem solving is a very important subject in the world of AI. In fact, a problem can be considered one or more goals along with a set of available interactions for reaching those goals. One of the best ways of solving AI problems is to use search methods. The SMA* (Simplified Memory Bounded A*) is one of the best methods of informed search. In this research, a hybrid method was proposed to increase the performance of SMA* search. The combining fuzzy logic with this search method and improving it with PSO algorithm brought satisfactory results. The use of fuzzy logic leads to increase the search flexibility especially when a robot dealing with lots of barriers and path changes. Furthermore, combining PSO saves the search from being trapped into local optimums and provides for search some correct and accurate suggestions. In the proposed algorithm, the results indicate that the cost of search and branching factor are decreased in comparison with other methods.rnrn
    Keywords: : Informed Search; Fuzzy Logic; Particle Swarm Optimization;Simplified Memory Bounded A*; Robot Navigation.

    by Vincent Ike Anireh, Emmanuel Ndidi Osegi 
    Abstract: In this paper, we present an open-source software tool ABC-PLOSS which is developed for use in optimization processes. Path-loss optimization deals with searching for the best set of operator-specific parameters in telecommunication that gives the least cost of operation. It is a primary issue that challenges mobile communication operators, particularly the Global System Mobile (GSM) operators in tuning mobile base station networks for efficient and reliable operation. The tool uses a sequential processor architecture based on a swarm intelligence algorithm called Artificial Bee Colony (ABC) and the Cost-231 Hata path-loss model as cost function for path-loss minimization. Using the ABC-PLOSS framework, the ABC algorithm is compared with two other existing and popular artificial intelligent (AI) algorithms called the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Results of simulation studies show that this tool is indeed useful as it gives a competitive or lower path-loss estimate when compared with conventional techniques. It also shows that it is possible for the ABC to attain an estimated 7-fold and 2-fold path-loss improvement over the GA and the PSO techniques respectively.
    Keywords: Artificial Bee Colony; Improvement factor; Minimization; Path-loss; Telecommunication networks; Global System Mobile.

  • An Improved Particle Swarm Optimization based Functional Link Artificial Neural Network Model for Software Cost Estimation   Order a copy of this article
    by ZAHID WANI, S.M.K Quadri 
    Abstract: Software cost estimation is the forecast of development effort and time needed to develop a software project. Estimation of software development effort and time is endlessly proving to be a difficult problem and thus catches the attention of most of the researchers from numerous fields of study. Recently, the usage of meta-heuristic techniques for software cost estimations is increasingly growing. So in this paper, we are proposing a technique which consists of Functional Link Artificial Neural Network Model and Particle Swarm Optimization algorithm as its training algorithm for achieving accurate estimates of a software project. Functional Link Artificial Neural Network is a high order feedforward artificial neural network consisting an input layer and an output layer. It reduces the computational complexity and has got the fast learning ability. Particle Swarm optimization algorithm on the other hand optimizes the problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. The proposed model has been evaluated using two basic evaluating criterias namely Magnitude of Relative Error and Median of Magnitude of Relative Error as a measure of performance index to simply weigh the obtained quality of estimation.
    Keywords: Software Cost Estimation; Artificial Neural Network; Functional Link Artificial Neural Network; Particle Swarm Optimization Algorithm; Improved Particle Swarm Optimization.