Concept Of Computational Intelligence Research Articles

A critical take on the role of random and local search-oriented components of modern computational intelligence-based optimization algorithms

The concept of computational intelligence (CI)-based optimization algorithms emerged in the early 1960s as a more practical approach to the contemporary derivate-based approaches. This paved the way for many modern algorithms to arise with an unprecedented growth rate in recent years, each claiming to have a novel and present a profound breakthrough in the field. That said, many have raised concerns about the performance of these algorithms and even identified fundamental flaws that could potentially undermine the integrity of their results. On that note, the premise of this study was to replicate some of the more prevalent, fundamental components of these algorithms in an abstract format as a measure to observe their behavior in an isolated environment. Six pseudo algorithms were designed to create a spectrum of intelligence behavior ranging from absolute randomness to local search-oriented computational architecture. These were then used to solve a set of centered and non-centered benchmark suites to see if statistically different patterns would emerge. The obtained result clearly highlighted that the algorithm’s performance would suffer significantly as these benchmarks got more intricate. This is not just in terms of the number of dimensions in the search space but also the mathematical structure of the benchmark. The implication is that, in some cases, sheer processing resources can mask the algorithm’s lack of sufficient intelligence. But as importantly, this study attempted to identify some mechanics and concepts that could potentially cause or amplify this problem. For instance, the excessive use of greedy strategy, a prevalent measure embedded in many modern CI-based algorithms, has been identified as potentially one of these reasons. The result, however, highlights a more fundamental problem in the CI-based optimization field. That is, these algorithms are often treated as a black box. This perception cultivated the culture of not exploring the underlying structure of these algorithms as long as they were deemed capable of generating acceptable results, which permits similar biases to go undetected.

COMPUTATIONAL INTELLIGENCE APPLIED IN OPTIMAL DESIGN OF WOODEN PLANE TRUSSES

The use of wood is widespread in rural constructions, and the truss systems stand out among its various applications. This specific system has several typologies and requires a thorough study to determine the most advantageous model for each project. The present study aims to apply the Computational Intelligence concepts to determine the minimum viable cross-section of a Howe truss. For the computational simulation, methods like Finite Elements were used to obtain the loads and the Firefly Algorithm for the optimization process, focusing on minimizing the total weight of the structural part. Studies were conducted varying the spans of the elements and the height to span ratio. The design assumptions for establishing the optimization method’s constraints follow the recommendations of the Brazilian standard for wood design, ABNT NBR 7190 (1997). Weights between 95.42 kg and 653.57 kg were obtained, and all optimization processes presented feasible solutions for the design constraints.

Message from Editors-in-Chief

It is our great pleasure to congratulate the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII) on its 20th anniversary. The concept of Computational Intelligence was originally authorized in the first IEEE World Congress on Computational Intelligence (WCCI) held in Orlando, Florida in 1994, where the key issues of CI were characterized by fuzzy, neuro, and EC (Evolutionary Computation). The JACI (Journal of Advanced Computational Intelligence) Vol.1 No.1 was published from Fuji Technology Press in Oct 1997, and the name of the journal has been changed to JACIII since Vol.7 No.1 Feb 2003 to accept more widely developed issues related to intelligent informatics. The JACIII is currently published in bimonthly, six issues a year, in cooperation with International Fuzzy Systems Association (IFSA), Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT), Brazilian Society of Automatics (SBA), The Society of Instrument and Control Engineers (SICE), John von Neumann Computer Society (NJSZT), Vietnamese Fuzzy Systems Society (VFSS), Fuzzy Systems and Intelligent Technologies Research Society of Thailand (FIRST), Korean Institute of Intelligent Systems (KIIS), and Taiwanese Association for Artificial Intelligence (TAAI), and is indexed in ESCI, SCOPUS, and COMPENDEX (Ei-Index). In the past 20 years personally, so many issues happened in the JACIII editing process. Some of them were very tough but all are very good memories to us. We occasionally organize the JACIII editorial meetings in Tokyo and remotely. As Editors-in-Chief, we are most grateful to all who have worked with the JACIII and helped reach its 20th anniversary. We thank the editorial board members for editing this journal with their high discernment and the guest editors for arranging special issues with their high profession. We would also like to thank the peer reviewers for their accurate evaluations in a short time, and finally our special thanks go to the editorial office of Fuji Technology Press Ltd., especially to its founder, Mr. K. Hayashi, and to the editors, Ms. Reiko Ohta and Mr. Kunihiko Uchida for their efforts in publishing this journal.

Delamination detection with error and noise polluted natural frequencies using computational intelligence concepts

Delaminations are one of the most prevalent defects noticed in laminated composite structures. The existence of delaminations changes the vibration characteristics of laminates and hence such indicators can be used to quantify the health characteristics of laminates and detect potential risk of catastrophic failures. An approach for online health monitoring of in-service composite laminates is presented in this paper that relies on methods based on computational intelligence. Typical changes in the observed vibration characteristics (i.e. change in natural frequencies) are considered as inputs to identify the existence, location and magnitude of delaminations. The performance of the proposed approach is demonstrated using both numerical models and experimental studies of composite laminates. Since this identification problem essentially involves the solution of an optimization problem, the use of finite element (FE) methods as the underlying tool for analysis turns out to be computationally expensive. A surrogate assisted optimization approach is hence introduced to contain the computational time within affordable limits. An artificial neural network (ANN) model with Bayesian regularization is used as the underlying approximation scheme while an improved rate of convergence is achieved using a memetic algorithm. However, building of ANN surrogate models usually requires large training datasets. K-means clustering is effectively employed to reduce the size of datasets. ANN is also used for the solution of the inverse problem to determine the interface, size and location of delaminations using changes in measured natural frequencies before and after damage. The algorithms successfully performed delamination detection given limited amount of training datasets. Since in all practical problems, noise and error are inherently present, the performance of the proposed approach is also evaluated under varying levels of noise and error. The results clearly highlight the efficiency and the robustness of the approach. Hence, a delamination prediction strategy via K-means clustering, ANN and optimization algorithms integrated with surrogate models based on ANN for computational enhancement have been successfully developed and found efficient for detection of the interface of delamination, its size and location in FRP composite laminates using variations in natural frequencies. The developed algorithms are applied to composite beams. Results clearly indicate the delamination detection capability of the algorithms. The results demonstrate that the proposed techniques provide remarkable accurate detection of delamination damage with error and noise corrupted natural frequency validation data. The algorithms are quite promising, successful, flexible and practical with significant increase in terms of efficiency and effectiveness for SHM.

FIF: A fuzzy information fusion algorithm based on multi-criteria decision making

The main goal of information fusion is to combine heterogeneous information to obtain a single composite of potential comparable alternative solutions that can be classified and ranked. The crux of information fusion, which is a type of data fusion, is threefold: (i) data must be comparable and numerical, using some normalization process; (ii) imprecision in data must be taken into consideration; (iii) an appropriate aggregation function to combine values into a single score must be selected.Recently, computational intelligence concepts and techniques to perform data/information fusion are emerging as suitable tools. Although with a different perspective, another field where much work has also been done for combining heterogeneous information is multi-criteria decision-making. In general, multi-criteria problems are modelled by choosing a set of relevant criteria – usually dealing with heterogeneous data – that have to be aggregated (i.e. fused) to obtain a single rating for each candidate alternative.In this paper we propose an algorithm for data/information fusion, which includes concepts from multi-criteria decision-making and computational intelligence, specifically, fuzzy multi-criteria decision-making and mixture aggregation operators with weighting functions. The application field of interest for this work is safe spacecraft landing with hazard avoidance; hence two existing hazard maps will be used to illustrate the versatility of the algorithm.

Validation of algorithms for delamination detection in composite structures using experimental data

This research work deals with aspects concerned with delamination detection in composite structures as revealed by an approach based on vibration measurements. Variations in vibration characteristics generated in composite laminates indicate the existence of delaminations because degradation due to delamination causes reduction in flexural stiffness and strength of the material and as a result vibration parameters like natural frequency responses are changed. Hence, it is possible to monitor the variation in natural frequencies to identify the presence of delamination, and assess its size and location for online structural health monitoring (SHM). The approach to this paper, therefore, typically depends on undertaking the analysis of structural models implemented by finite element analysis (FEA). The numerical solutions using FE models known as the simulator computes the natural frequencies for the delaminated and undelaminated specimens of composite laminates. However, these FE models are computationally expensive, and surrogate (approximation) models are introduced to curtail the computational expense. The simulator is employed to solve the inverse problem using algorithms based on computational intelligence concepts. An artificial neural network (ANN) model is developed to also solve the inverse problem for delamination detection directly and to provide surrogate models integrated with optimization algorithms (the gradient-based local search and non-dominated sorting genetic algorithm-II) to contain the computationally expensive simulations by FEA. This approach is termed as surrogate assisted optimization and it is seen that the engagement of surrogate models in lieu of the FE models in the optimization loop greatly enhances the accuracy of delamination detection results within an affordable computational cost and provides control over handling different variables. Meanwhile, to aid with the building of effective surrogate models using substantial number of training datasets, K-means clustering algorithm is harnessed and this effectively reduces the large training datasets usually required for ANN training. This paper demonstrated that ANN and optimization algorithms with surrogates show immense potentialities for use in delamination damage detection scenarios. Prediction errors of the algorithms were quantified and they were shown to be satisfactory when applied to previously experimental data. The algorithms in their inverse formulations are capable of predicting accurately delamination parameters. Hence, these algorithms should be employed for application in the domain of SHM where their small computational requirements could be exploited for online damage detection.

Cooperative, hybrid agent architecture for real-time traffic signal control

This paper presents a new hybrid, synergistic approach in applying computational intelligence concepts to implement a cooperative, hierarchical, multiagent system for real-time traffic signal control of a complex traffic network. The large-scale traffic signal control problem is divided into various subproblems, and each subproblem is handled by an intelligent agent with a fuzzy neural decision-making module. The decisions made by lower-level agents are mediated by their respective higher-level agents. Through adopting a cooperative distributed problem solving approach, coordinated control by the agents is achieved. In order for the multiagent architecture to adapt itself continuously to the dynamically changing problem domain, a multistage online learning process for each agent is implemented involving reinforcement learning, learning rate and weight adjustment as well as dynamic update of fuzzy relations using an evolutionary algorithm. The test bed used for this research is a section of the Central Business District of Singapore. The performance of the proposed multiagent architecture is evaluated against the set of signal plans used by the current real-time adaptive traffic control system. The multiagent architecture produces significant improvements in the conditions of the traffic network, reducing the total mean delay by 40% and total vehicle stoppage time by 50%.

Message from Editors-in-Chief

Message from Editors-in-Chief