A comparative study of fuzzy parameter control in a general purpose local search metaheuristic

There is a growing number of studies on general purpose metaheuristics that are directly applicable to multiple domains. Parameter setting is a particular issue considering that many of such search methods come with a set of parameters to be configured. Fuzzy logic has been used extensively in control applications and is known for its ability to handle uncertainty. In this study, we investigate the potential of using fuzzy systems to control the parameter settings of a threshold accepting (TA) metaheuristic for improving the overall effectiveness of a cross-domain approach. We have evaluated the performance of various general purpose local search metaheuristics which mix multiple heuristics at random and apply the TA metaheuristic with fixed threshold, crisp (non-fuzzy) rule-based control of the threshold and various fuzzy systems controlling the threshold. The empirical results show that the approach using the TA with crisp rule-based control performs the best across six problem domains from a benchmark

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

An innovative metaheuristic strategy for solar energy management through a neural networks framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development

Comparative Analysis of Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem

This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric

Modelling and Tracking of the Global Maximum Power Point in Shaded Solar PV Systems Using Computational Intelligence

Solar Photovoltaic (PV) systems are renewable energy sources that are environmentally friendly and are now widely used as a source of power generation. The power produced by solar PV varies with temperature, solar irradiance and load. This variation is nonlinear and it is difficult to predict how much power will be produced by the solar PV system. When the solar panel is directly coupled to the load, the power delivered is not optimal unless the load is properly matched to the PV system. In the case of a matched load the variation of irradiance and temperature will change this matching so a maximum peak power point tracking is therefore necessary for maximum efficiency. The complete PV system with a maximum power point tracking (MPPT) includes the solar panel array, MPPT algorithm and a DC-DC converter topology. Each subsystem is modelled and simulated in MATLAB/Simulink environment. The components are then combined with a DC resistive load to assess the overall performance when the PV panels are subjected to different weather conditions. The PV panel is modelled based on the Shockley diode equation and is used to predict the electrical characteristic curves under different irradiances and temperatures. In this dissertation, five MPPT algorithms were investigated. These algorithms include the standard Perturb and Observe (PnO), Incremental conductance (IC), Fuzzy Logic (FL), Particle Swarm Optimisation (PSO) and the Firefly Optimisation (FA). The algorithms are tested under different weather conditions including partial shading. The Particle Swarm and Firefly algorithm performed relatively the same and were chosen to be the best under all test conditions as they were the most efficient and were able to track the global maximum power point under partial shading. The PnO and IC performed well under static and varying irradiance, the PnO was seen to lose track of the MPP under rapid increasing irradiance. The PnO was tested under partial shaded conditions and it was seen that it is not reliable under these conditions. The Fuzzy logic performed better than the PnO and IC but was not as good as the PSO and FA. Since the fuzzy logic requires extensive tuning to converge it was not tested under partial shaded conditions. A DC-DC boost converter interface study between a DC source and the DC load are performed. This includes the steady state and dynamic analysis of the Boost converter. The converter is linearised about its steady state operating point and the transfer function is obtained using the state space averaged model. The simulation results of the complete PV system show that PSO and Firefly algorithm provided the best results under all weather conditions compared to other algorithms. They provided less oscillations at steady state, high efficiency in tracking (99%), quick convergence time at maximum power point and where able to track global power under partial shaded weather conditions for all partial shaded patterns. The Fuzzy logic performed well for what it was tested for which are static irradiance and rapid varying irradiance. The PnO and IC also performed relatively well but showed a lot of ringing at steady state. The PnO failed to track the MPP at certain instances under rapid increasing irradiance and the IC was shown to be unstable at low irradiance. The PnO was not reliable in tracking the global maximum power point under partial shaded conditions as it converged at local maximum power points for some partial shaded patterns