International Journal of Swarm Intelligence (7 papers in press)
Concurrent parametric optimization of single pass end milling through GRA coupled with PSO for calmax-635 die steel
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
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.
Two swarm intelligence based approaches for the p-center problem
by B. Jayalakshmi, Alok Singh
Abstract: The p-center problem is an important facility location problem. In this problem, the objective is to find a set Y of p vertices on an undirected weighted graph G = (V, E) in such a way that Y is a subset of V and the maximum distance over all the distances from vertices to their closest vertices in Y is minimized. The vertices in set Y are called centers. In this paper, we have proposed two swarm intelligence based approaches for the p-center problem. The first approach is based on artificial bee colony (ABC) algorithm, whereas the latter approach is based on invasive weed optimization (IWO) algorithm. The ABC algorithm and IWO algorithm are relatively new metaheuristic techniques inspired respectively from collective intelligent behavior shown by honey bees while foraging and the sturdy process of weed colonization & dispersion in an ecosystem. Computational results on the well-known benchmark instances of p-center problem show the effectiveness of our approaches in finding high quality solutions.
Keywords: artificial bee colony algorithm; facility location problem; invasive weed optimization algorithm; p-center problem; swarm intelligence.
A self-tuning Firefly Algorithm to tune the parameters of Ant Colony System (ACSFA)
by Anuradha Ariyaratne, T.G.I. Fernando, Sunethra Weerakoon
Abstract: Ant colony system (ACS) is a promising approach which has been widely used in problems such as Travelling Salesman Problems (TSP), Job shop scheduling problems (JSP) and Quadratic Assignment problems (QAP). In its original implementation, parameters of the algorithm were selected by trial and error approach. Over the last few years, novel approaches have been proposed on adapting the parameters of ACS in improving its performance. The aim of this paper is to use a framework introduced for self-tuning optimization algorithms combined with the firefly algorithm (FA) to tune the parameters of theACSsolving symmetric TSP problems. The FA optimizes the problem specific parameters of ACS while the parameters of the FA are tuned by the selected framework itself. With this approach, the user neither has to work with the parameters of ACS nor the parameters of FA. Using common symmetric TSP problems we demonstrate that the framework fits well for the ACS. A detailed statistical analysis further verifies the goodness of the new ACS over the existing ACS and also of the other techniques used to tune the parameters of ACS.
Keywords: Self tuning framework; Ant Colony System; Travelling Salesman problem; Firefly Algorithm.
Effective search technique in teaching and learning phase of TLBO algorithm for numerical function optimization
by Jaydeep Patel, Vimal Savsani, Vivek Patel, Rajesh Patel
Abstract: Optimization is a very important process and plays a very vital role in many engineering and scientific researches. All optimization algorithms have different search tendency to find the optimum value in the design space. However, the capability of the metaheuristic can be enhanced by modifying it with other efficient search techniques to make it more efficient and computationally effective. This paper explores the modifications in the basic teaching learning based optimization (TLBO) algorithm with different effective search technique inspired from artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms for further enhancing the search capability of TLBO. To check the effectiveness of the proposed algorithm, 55 different benchmark problems from CEC2005 and CEC2014 were used. The proposed algorithm is also compared with other well-known metaheuristic methods. Statistical analysis is performed by Friedman rank test. The numerical comparison shows that the proposed algorithms are an alternative, effective and competitive optimization algorithm for continuous problems.
Keywords: Teaching-learning based optimization (TLBO); Hybrid Metaheuristic; Artificial bee colony (ABC) optimization; particle swarm optimization (PSO); unconstrained optimization.
Special Issue on: Computational Intelligence Theory, Methods and Applications
Performance Evaluation of Pitch Values for Finding Emotions in Tamil Speech
Abstract: All living creatures in this world have emotions naturally like humans, birds, animals, fishes, plants and so on. Sometimes, non-living things also can express sentimental emotions. Lot of researches and epic writers have written about emotions in different angles. The humans emotions may differ or expose based on culture, life style, living habits, food habits, behavioural habits, experience and knowledge. Emotional expression may be differing based on experience and interpretations. Pitch of vocal speech is a significant parameter for categorising emotions in speech. This paper is special for finding the pitch values in Tamil speech(voice modulation), frequency of speech, length of frequency and for the Tamil speechs recorded voice to text conversion.
Keywords: Tamil; Emotions; Pitch; Recognition; Text; Analysis.
A classed approach towards rainfall forecasting: Machine learning method
by Shanu Khan, Vikram Kumar, Sandeep Chaurasia
Abstract: The interest for precipitation anticipating has turned into a huge element in the outline of rainfall runoff and other hydrological models. As of now, the artificial neural system (ANN) is the most well-known model that is utilized to evaluate rainfall using different climatic parameters. However, classed approach, called the extreme learning machine (ELM) algorithm, has been introduced in this present paper and ELM-based learning framework is used to predict rainfall-runoff forecasting. Extreme learning machine algorithm is much faster as compared to the artificial neural system, and outcomes in a high generalization competence. In view of these outcomes we assert that out of the machine learning calculations tried, the ELM was the more expeditious tool for the forecast of rainfall and its related properties.
Keywords: data mining; artificial neural network; ANN; extreme learning
machine; ELM; rainfall-runoff prediction.