Multiobjective programming for type-2 hierarchical fuzzy inference trees

This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum tree-like structure. Specifically, a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure provides a high degree of approximation accuracy. The construction of HFIT takes place in two phases. Firstly, a nondominated sorting based multiobjective genetic programming (MOGP) is applied to obtain a simple tree structure (low model’s complexity) with a high accuracy. Secondly, the differential evolution algorithm is applied to optimize the obtained tree’s parameters. In the obtained tree, each node has a different input’s combination, where the evolutionary process governs the input’s combination. Hence, HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP for the tree’s structural optimization that accept inputs only relevant to the knowledge contained in data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared both theoretically and empirically with recently proposed FISs methods from the literature, such as McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the obtained results, it was found that the HFIT provided less complex and highly accurate models compared to the models produced by most of the other methods. Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selectio

Metaheuristic Based Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems

Scheduling is a key problem in distributed heterogeneous computing systems in order to benefit from the large computing capacity of such systems and is an NP-complete problem. In this paper, we present a metaheuristic technique, namely the Particle Swarm Optimization (PSO) algorithm, for this problem. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. The scheduler aims at minimizing makespan, which is the time when finishes the latest task. Experimental studies show that the proposed method is more efficient and surpasses those of reported PSO and GA approaches for this problem.This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Antlion optimization algorithm for optimal non-smooth economic load dispatch

This paper presents applications of Antlion optimization algorithm (ALO) for handling optimal economic load dispatch (OELD) problems. Electricity generation cost minimization by controlling power output of all available generating units is a major goal of the problem. ALO is a metaheuristic algorithm based on the hunting process of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each studied case has different objective function and complex level of restraints. Three test cases are employed and arranged according to the complex level in which the first one only considers multi fuel sources while the second case is more complicated by taking valve point loading effects into account. And, the third case is the highest challenge to ALO since the valve effects together with ramp rate limits, prohibited operating zones and spinning reserve constraints are taken into consideration. The comparisons of the result obtained by ALO and other ones indicate the ALO algorithm is more potential than most methods on the solution, the stabilization, and the convergence velocity. Therefore, the ALO method is an effective and promising tool for systems with multi fuel sources and considering complicated constraints

Ensemble of heterogeneous flexible neural tree for the approximation and feature-selection of Poly (Lactic-co-glycolic Acid) micro-and nanoparticle

In this work, we used an adaptive feature-selection and function approximation model, called, flexible neural tree (FNT) for predicting Poly (lactic-co-glycolic acid) (PLGA) micro-and nanoparticle's dissolution-rates that bears a significant role in the pharmaceutical, medical, and drug manufacturing industries. Several factor influences PLGA nanoparticles dissolution-rate prediction. FNT model enables us to deal with feature selection and prediction simultaneously. However, a single FNT model may or may not offer a generalized solution. Hence, to build a generalized model, we used an ensemble of FNTs. In this work, we have provided a comprehensive study for examining the most significant (influencing) features that influences dissolution rate prediction

Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach

Selection of a representative set of features is still a crucial and challeng- ing problem in machine learning. The complexity of the problem increases when any of the following situations occur: a very large number of at- tributes (large dimensionality); a very small number of instances or time points (small-instance set). The rst situation poses problems for machine learning algorithm as the search space for selecting a combination of relevant features becomes impossible to explore in a reasonable time and with rea- sonable computational resources. The second aspect poses the problem of having insu cient data to learn from (insu cient examples). In this work, we approach both these issues at the same time. The methods we proposed are heuristics inspired from nature (in particular, from biology). We pro- pose a hybrid of two methods which has the advantage of providing a good learning from fewer examples and a fair selection of features from a really large set, all these while ensuring a high standard classi cation accuracy of the data. The methods used are antlion optimization (ALO), grey wolf opti- mization (GWO), and a combination of the two (ALO-GWO). We test their performance on datasets having almost 50,000 features and less than 200 instances. The results look promising while compared with other methods such as genetic algorithms (GA) and particle swarm optimization (PSO)

Metaheuristic tuning of type-II fuzzy inference systems for data mining

Introduction of fuzzy set enabled the modeling of uncertain and noisy information. Type-2 fuzzy set took this further ahead by allowing type-2 fuzzy membership function to be fuzzy itself. In this work, we describe an interval type-2 fuzzy logic system (FLS). The training of interval type-2 FLS was provided in a supervised manner by using metaheuristic algorithms. We comprehensively illustrated formulation of interval type-2 FLS into an optimization problem. A precise genotype (a real vector) mapping of FLS was described. This work finds the extent of the learning capability of FLS. Since the FLS learning is computationally difficult and costly, which we described in detail in this work, a comprehensive comparison between the performances of the metaheuristic algorithms was offered. The obtained results suggest that FLS learning was faster at the initial iterations of the metaheuristic learning, but tend to slow and get stuck in local minima. However, the metaheuristic algorithms, differential evaluation and bacteria foraging optimization offered significantly better results when compared to artificial bee colony, gray wolf optimization, and particle swarm optimization