State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems - a review

Given the considerable recent attention to distributed power generation and interest in sustainable energy, the integration of photovoltaic (PV) systems to grid-connected or isolated microgrids has become widespread. In order to maximize power output of PV system extensive research into control strategies for maximum power point tracking (MPPT) methods has been conducted. According to the robust, reliable, and fast performance of artificial intelligence-based MPPT methods, these approaches have been applied recently to various systems under different conditions. Given the diversity of recent advances to MPPT approaches a review focusing on the performance and reliability of these methods under diverse conditions is required. This paper reviews AI-based techniques proven to be effective and feasible to implement and very common in literature for MPPT, including their limitations and advantages. In order to support researchers in application of the reviewed techniques this study is not limited to reviewing the performance of recently adopted methods, rather discusses the background theory, application to MPPT systems, and important references relating to each method. It is envisioned that this review can be a valuable resource for researchers and engineers working with PV-based power systems to be able to access the basic theory behind each method, select the appropriate method according to project requirements, and implement MPPT systems to fulfill project objectives.

Evolutionary multiobjective optimization in engineering management: an empirical study on bridge deck rehabilitation

There exist multiple objectives in engineering management such as minimum cost and maximum service capacity. Although solution methods of multiobjective optimization problems have undergone continual development over the past several decades, the methods available to date are not particularly robust, and none of them performs well on the broad classes. Because genetic algorithms work with a population of points, they can capture a number of solutions simultaneously, and easily incorporate the concept of Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with the rehabilitation planning of bridge decks at a network level by minimizing the rehabilitation cost and deterioration degree simultaneously.

Evolutionary multiobjective optimization in engineering management: an empirical application in infrastructure systems

Generally multiple objectives exist in transportation infrastructure management, such as minimum cost and maximum service capacity. Although solution methoak of multiobjective optimization problems have undergone continual development over the part several decades, the methods available to date are not particularly robust, and none of them perform well on the broad classes. Because genetic algorithms work with apopulation ofpoints, they can capture a number of solutions simultaneously, and easily incorporate the concept of a Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with an empirical application for the rehabilitation planning of bridge decks, at a network level, by minimizing the rehabilitation cost and deterioration degree simultaneously.

Evolutionary optimization: a big data perspective

Stochastic search techniques such as evolutionary algorithms (EA) are known to be better explorer of search space as compared to conventional techniques including deterministic methods. However, in the era of big data like most other search methods and learning algorithms, suitability of evolutionary algorithms is naturally questioned. Big data pose new computational challenges including very high dimensionality and sparseness of data. Evolutionary algorithms' superior exploration skills should make them promising candidates for handling optimization problems involving big data. High dimensional problems introduce added complexity to the search space. However, EAs need to be enhanced to ensure that majority of the potential winner solutions gets the chance to survive and mature. In this paper we present an evolutionary algorithm with enhanced ability to deal with the problems of high dimensionality and sparseness of data. In addition to an informed exploration of the solution space, this technique balances exploration and exploitation using a hierarchical multi-population approach. The proposed model uses informed genetic operators to introduce diversity by expanding the scope of search process at the expense of redundant less promising members of the population. Next phase of the algorithm attempts to deal with the problem of high dimensionality by ensuring broader and more exhaustive search and preventing premature death of potential solutions. To achieve this, in addition to the above exploration controlling mechanism, a multi-tier hierarchical architecture is employed, where, in separate layers, the less fit isolated individuals evolve in dynamic sub-populations that coexist alongside the original or main population. Evaluation of the proposed technique on well known benchmark problems ascertains its superior performance. The algorithm has also been successfully applied to a real world problem of financial portfolio management. Although the proposed method cannot be considered big data-ready, it is certainly a move in the right direction.

Towards optimization of a real-world robotic-sensor system of systems

The problem of threat detection in an unstructured environment is considered. Three systems, comprising of robots and sensors, are proposed to form a system of systems (SoS) to find a solution to the problem. System interactions are defined to provide a framework for formulation as an SoS optimization problem. Different cost and objective functions are introduced for optimization of local criteria. Using different weights, a linear combination of the local cost and objective functions is obtained to propose a global objective function. An algorithm is suggested to find an optimum value for the global objective function leading towards optimization of the SoS.

An evolutionary computing approach to minimize dynamic hedging error

The objective of our present paper is to derive a computationally efficient genetic pattern learning algorithm to evolutionarily derive the optimal rebalancing weights (i.e. dynamic hedge ratios) to engineer a structured financial product out of a multiasset, best-of option. The stochastic target function is formulated as an expected squared cost of hedging (tracking) error which is assumed to be partly dependent on the governing Markovian process underlying the individual asset returns and partly on
randomness i.e. pure white noise. A simple haploid genetic algorithm is advanced as an alternative numerical scheme, which is deemed to be
computationally more efficient than numerically deriving an explicit solution to the formulated optimization model. An extension to our proposed scheme is suggested by means of adapting the Genetic Algorithm parameters based on fuzzy logic controllers.

Optimization of mixing parameters through a water model for metal matrix composites synthesis

The influence of the mixing parameters on the synthesis of Al–SiCp reinforced metal matrix composites (MMCs) by the stir casting technique is investigated through a water model. The effects of some important mixing parameters such as impeller blade angle, rotating speed, direction of impeller rotation and effect of baffles are investigated and optimized. The results have shown that the axial concentration variation of natural graphite during stirring in the presence of four vertical baffles is 1.0 wt% against in the absence of baffles it is increased to 2.3 wt%. The variations observed in natural graphite concentration in water during mixing are in close agreement with the earlier modeling and limited experimental studies reported on the real molten aluminum–SiC system. Semi-empirical correlations arrived at between the dimensionless numbers for stirred water – natural graphite slurries are Po = Re−0.0545 Fr−1.099 and Po = Re−0.0219 Fr−1.0382 for clockwise and counter clockwise rotation respectively.

Optimization-based formation of autonomous mobile robots

The formation of autonomous mobile robots to an arbitrary geometric pattern in a distributed fashion is a fundamental problem in formation control. This paper presents a new asynchronous, memoryless (oblivious) algorithm to the formation problem via distributed optimization techniques. The optimization minimizes an appropriately defined difference function between the current robot distribution and the target geometric pattern. The optimization processes are performed independently by individual robots in their local coordinate systems. A movement strategy derived from the results of the distributed optimizations guarantees that every movement makes the current robot configuration approaches the target geometric pattern until the final pattern is reached.

Introduction to evolutionary computing in system design

In this chapter, an introduction on the use of evolutionary computing techniques, which are considered as global optimization and search techniques inspired from biological evolutions, in the domain of system design is presented. A variety of evolutionary computing techniques are first explained, and the motivations of using evolutionary computing techniques in tackling system design tasks are then discussed. In addition, a number of successful applications of evolutionary computing to system design tasks are described.

A modified micro genetic algorithm for undertaking multi-objective optimization problems

In this paper, a Modified micro Genetic Algorithm (MmGA) is proposed for undertaking Multi-objective Optimization Problems (MOPs). An NSGA-II inspired elitism strategy and a population initialization strategy are embedded into the traditional micro Genetic Algorithm (mGA) to form the proposed MmGA. The main aim of the MmGA is to improve its convergence rate towards the pareto optimal solutions. To evaluate the effectiveness of the MmGA, two experiments using the Kursawe test function in MOPs are conducted, and the results are compared with those from other approaches using a multi-objective evolutionary algorithm indicator, i.e. the Generational Distance (GD). The outcomes positively demonstrate that the MmGA is able to provide useful solutions with improved GD measures for tackling MOPs.

Multi-objective image segmentation with an interactive evolutionary computation approach

In this paper, a multi-objective image segmentation approach with an Interactive Evolutionary Computation (IEC)-based framework is presented. Two objectives, i.e., the overall deviation and the connectivity measure, are optimized simultaneously using a mu

Classification and optimization of product review information using soft computing models

A soft computing framework to classify and optimize text-based information extracted from customers' product reviews is proposed in this paper. The soft computing framework performs classification and optimization in two stages. Given a set of keywords extracted from unstructured text-based product reviews, a Support Vector Machine (SVM) is used to classify the reviews into two categories (positive and negative reviews) in the first stage. An ensemble of evolutionary algorithms is deployed to perform optimization in the second stage. Specifically, the Modified micro Genetic Algorithm (MmGA) optimizer is applied to maximize classification accuracy and minimize the number of keywords used in classification. Two Amazon product reviews databases are employed to evaluate the effectiveness of the SVM classifier and the ensemble of MmGA optimizers in classification and optimization of product related keywords. The results are analyzed and compared with those published in the literature. The outputs potentially serve as a list of impression words that contains useful information from the customers' viewpoints. These impression words can be further leveraged for product design and improvement activities in accordance with the Kansei engineering methodology.