Forthcoming articles


International Journal of Swarm Intelligence


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International Journal of Swarm Intelligence (9 papers in press)


Regular Issues


    by Marie-Laure Bouchet 
    Abstract: Love sak
    Keywords: sake; japan;matsuri.
    This article is being considered for Open AccessThis article is being considered for Open Access
  • 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.

  • Comparison of Cuckoo Search and Particle Swarm Optimization in Triclustering Temporal Gene Expression Data   Order a copy of this article
    by Swathypriyadharsini Palaniswamy, Premalatha K 
    Abstract: The nature inspired metaheuristic algorithms have ubiquitous nature in nearly every aspect, where computational intelligence is applied. This paper focuses on the comparative study of two commonly used robust bio inspired optimization algorithms namely Cuckoo Search and Particle Swarm Optimization for triclustering the microarray gene expression data. Triclustering broadens the clustering technique by extracting the subset of genes that are highly coexpressed over a subset of conditions and across a subset of time points. Both the algorithms are applied to three real life three dimensional datasets. The performances of the algorithms are compared using the Mean Square Residue as a fitness function and it is also compared with other triclustering algorithms. The experiment results prove that cuckoo search algorithm has better computational efficiency than Particle Swarm Optimization algorithm.
    Keywords: Cuckoo Search; Particle Swarm Optimization; Triclustering; Microarray Gene Expression Data; Temporal Data Analysis.

  • Transmission Network Expansion Planning using State of Art Nature Inspired Algorithms : A Survey   Order a copy of this article
    by Ashish Khandelwal, Annapurna Bhargava, Ajay Sharma, Harish Sharma 
    Abstract: Transmission network expansion planning (TNEP) problem has been continuously solved for many years still the cost effective, reliable, and optimize solution is always desirable. The TNEP has been solved by various conventional and non conventional strategies. The strategy to find the solution of TNEP by classical mathematical optimization techniques is tedious, slow and inefficient. In recent years, nature inspired algorithms (NIAs) have proven their importance to provide the solutions of the TNEP problem over classical mathematical optimization techniques. This paper presents a review on the key contributions of the state-of-art NIAs to solve the TNEP problem. Further, the TNEP system specific significant works presented in the literature are summarised for easy understanding of the readers. The readers can get a brief description of the considered NIAs algorithms which has been applied to solve various systems of TNEP problem and they can also identify the significant NIA which is being applied for specific TNEP system
    Keywords: Genetic Algorithm; Particle Swarm Optimization; Differential Evolution; Artificial Bee Colony Algorithm; Ant Colony Optimization; Harmony Search Algorithm, Spider Monkey Optimization; Grey Wolf Optimization.

Special Issue on: Computational Intelligence

  • Fractional Order Ant Colony Control with Genetic Algorithm based Initialization   Order a copy of this article
    by Ambreesh Kumar, Varun Upadhaya, Ayush Singh, Paras Pandey, Rajneesh Sharma 
    Abstract: Initializing parameters of the Ant Colony Optimization (ACO) presents a formidable research lacuna in the design of ACO based control. So far, initial ACO parameters have been set using a trial and error procedure and sometimes with some heuristics. However, these approaches lead to slow convergence and sometimes even divergence of the designed fractional order controller. Our proposed approach advocates a genetic algorithm (GA) based initialization of the ACO parameters for efficient and effective design of the ACO based fractional order controllers. We minimize a multi objective function via a nested GA technique for designing the GA based ACO fractional order PID controller. This brings in an element of certainty in the performance of the designed ACO controller. The controller has been validated on seven fractional order systems with comparative evaluation against: a) ACO control and, b) GA based fractional order control. Our GA based ACO controller is able to garner better transient response parameters (rise time, peak overshoot) along with an excellent steady state performance as signified by (settling time, ITAE). A flip side though, is higher computational complexity (easily tackled by high performance machines) of our approach which is mitigated to a certain extent by its superior performance.
    Keywords: Genetic Algorithm; Ant Colony Control; Fractional Order system; GA based ACO.

  • Optimizing Fracture in Automotive Tail Cap by Firefly Algorithm   Order a copy of this article
    by Ganesh Kakandikar, Omkar Kulkarni, Sujata Patekar, Trupti Bhoskar 
    Abstract: Deep drawing is a manufacturing process in which sheet metal is progressively formed into a three-dimensional shape through the mechanical action of a die forming the metal around a punch. The deep drawing process contains many components and steps. Pots, pans for cooking, containers, sinks, automobile parts such as panels and gas tanks are among a few of the items manufactured by sheet metal deep drawing. The Predominant failure modes in sheet metal parts (deep drawing process) are fracture. The prediction and prevention of fracture are extremely important in the design of tooling and process parameters in deep drawing process. Fracture or necking occurs in a drawn part, which is under excessive tensile stresses. Fractures are the important defects in deep drawing operation, which can be prevented using blank holding force. Fracture limit depends on various tooling, process and material parameters. Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behaviour in nature. Each firefly movement is based on absorption of the other one.
    Keywords: Firefly algorithm; deep Drawing Process; Optimization; Fracture.