International Journal of Intelligent Computing and Cybernetics 

About: International Journal of Intelligent Computing and Cybernetics is an academic journal published by Emerald Publishing Limited. The journal publishes majorly in the area(s): Computer science & Fuzzy logic. It has an ISSN identifier of 1756-378X. Over the lifetime, 432 publications have been published receiving 4605 citations.

Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning

Abstract: Purpose – The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems. Design/methodology/approach – The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted. Findings – The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases. Originality/value – In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is prese...

Colonial competitive algorithm A novel approach for PID controller design in MIMO distillation column process

Abstract: Purpose – This paper aims to describe colonial competitive algorithm (CCA), a novel socio‐politically inspired optimization strategy, and how it is used to solve real world engineering problems by applying it to the problem of designing a multivariable proportional‐integral‐derivative (PID) controller Unlike other evolutionary optimization algorithms, CCA is inspired from a socio‐political process – the competition among imperialists and colonies In this paper, CCA is used to tune the parameters of a multivariable PID controller for a typical distillation column processDesign/methodology/approach – The controller design objective was to tune the PID controller parameters so that the integral of absolute errors, overshoots and undershoots be minimized This multi‐objective optimization problem is converted to a mono‐objective one by adding up all the objective functions in which the absolute integral of errors is emphasized to be reduced as long as the overshoots and undershoots remain acceptableFindin

Intelligent water drops algorithm: A new optimization method for solving the multiple knapsack problem

Abstract: Purpose – The purpose of this paper is to test the capability of a new population‐based optimization algorithm for solving an NP‐hard problem, called “Multiple Knapsack Problem”, or MKP.Design/methodology/approach – Here, the intelligent water drops (IWD) algorithm, which is a population‐based optimization algorithm, is modified to include a suitable local heuristic for the MKP. Then, the proposed algorithm is used to solve the MKP.Findings – The proposed IWD algorithm for the MKP is tested by standard problems and the results demonstrate that the proposed IWD‐MKP algorithm is trustable and promising in finding the optimal or near‐optimal solutions. It is proved that the IWD algorithm has the property of the convergence in value.Originality/value – This paper introduces the new optimization algorithm, IWD, to be used for the first time for the MKP and shows that the IWD is applicable for this NP‐hard problem. This research paves the way to modify the IWD for other optimization problems. Moreover, it opens...

Solving two‐armed Bernoulli bandit problems using a Bayesian learning automaton

Abstract: Purpose – The two‐armed Bernoulli bandit (TABB) problem is a classical optimization problem where an agent sequentially pulls one of two arms attached to a gambling machine, with each pull resulting either in a reward or a penalty. The reward probabilities of each arm are unknown, and thus one must balance between exploiting existing knowledge about the arms, and obtaining new information. The purpose of this paper is to report research into a completely new family of solution schemes for the TABB problem: the Bayesian learning automaton (BLA) family.Design/methodology/approach – Although computationally intractable in many cases, Bayesian methods provide a standard for optimal decision making. BLA avoids the problem of computational intractability by not explicitly performing the Bayesian computations. Rather, it is based upon merely counting rewards/penalties, combined with random sampling from a pair of twin Beta distributions. This is intuitively appealing since the Bayesian conjugate prior for a bino...

Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0

Abstract: In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.,In the competitive growth and industries 4.0, the prediction in the cost plays a key role.,At the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.,The prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.