Contrast and brigtness balance in image enhancement using Cuckoo Search-optimized image fusion
Many vision-based systems suffer from poor levels of contrast and brightness, mainly because of inadequate and improper illumination during the image acquisition process. As a result, the required specified information from the acquired image is not available for the particular application. In general, it is hard to achieve a balance between the improvement of contrast and brightness in image enhancement. By introducing nature-inspired optimization in image enhancement, the best features of the image are utilized, and the complexity related to the nonlinearity of images can be solved with various constraints, like a balance between contrast and brightness. In this work, a novel automatic method for image enhancement to find a balance between contrast and brightness is developed by using Cuckoo Search-optimized image fusion. First, the Cuckoo Search-based optimization algorithm generates two sets of optimized parameters. These parameter sets are used to generate a pair of enhanced images, one with a high degree of sharpness and contrast, the other is bright and has been improved without losing the level of detail. Furthermore, the two enhanced images are fused by the fusion process to obtain an output image where the contrast and brightness are in balance. The effectiveness of the proposed method is verified by applying it to standard images (CVG-UGR image database) and lathe tool images. Experimental results demonstrated that the proposed method performs better with regard to both the quality of contrast and brightness, moreover, yields enhanced quality evaluation metrics compared to the other conventional techniques.
Genetic programming-based symbolic regression for goal-oriented dimension reduction
The majority of dimension reduction techniques are built upon the optimization of an objective functionaiming to retain certain characteristics of the projected datapoints: the variance of the original dataset,the distance between the datapoints or their neighbourhood characteristics, etc. Building upon theoptimization-based formalization of dimension reduction techniques, the goal-oriented formulation ofprojection cost functions is proposed. For the optimization of the application-oriented data visualizationcost function, a Multi-gene genetic programming (GP)-based algorithm is introduced to optimize thestructures of the equations used for mapping high-dimensional data into a two-dimensional space andto select variables that are needed to explore the internal structure of the data for data-driven softwaresensor development or classifier design. The main benefit of the approach is that the evolved equationsare interpretable and can be utilized in surrogate models. The applicability of the approach is demon-strated in the benchmark wine dataset and in the estimation of the product quality in a diesel oil blendingtechnology based on an online near-infrared (NIR) analyzer. The results illustrate that the algorithm iscapable to generate goal-oriented and interpretable features, and the resultant simple algebraic equa-tions can be directly implemented into applications when there is a need for computationally cost-effective projections of high-dimensional data as the resultant algebraic equations are computationallysimpler than other solutions as neural networks.
Genetic programming-based development of thermal runaway criteria
Common thermal runaway criteria (e.g. divergence criterion and the Maxi criterion) may predict a thermal runaway unreasonably as the Maximum Allowable Temperature (MAT) is not taken into account. This contribution proposes a method for the goal-oriented construction of reactor runaway criteria by Genetic Programming (GP). The runaway prediction problem is formulated as a critical equation-based classification task, and GP is used to identify the optimal structure of the equations that also take into account the MAT. To demonstrate the applicability of the method, tailored criteria were developed for batch and continuous stirred-tank reactors. The resultant critical equations outperform the well-known criteria in terms of the early and accurate indication of thermal runaways.
Hierarchical Representation Based Constrained Multi-objective Evolutionary Optimisation of Molecular Structures
We propose an efficient algorithm to generate Pareto optimal set of reliable molecular structures represented by group contribution methods. To effectively handle structural constraints we introduce goal oriented genetic operators to the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The constraints are defined based on the hierarchical categorisation of the molecular fragments. The efficiency of the approach is tested on several benchmark problems. The proposed approach is highly efficient to solve the molecular design problems, as proven by the presented benchmark and refrigerant design problems.
Group Contribution Method-based Multi-objective Evolutionary Molecular Design
The search for compounds exhibiting desired physical and chemical properties is an essential, yet complex problem in the chemical, petrochemical, and pharmaceutical industries. During the formulation of this optimization-based design problem two tasks must be taken into consideration: the automated generation of feasible molecular structures and the estimation of macroscopic properties based on the resultant structures. For this structural characteristic-based property prediction task numerous methods are available. However, the inverse problem, the design of a chemical compound exhibiting a set of desired properties from a given set of fragments is not so well studied. Since in general design problems molecular structures exhibiting several and sometimes conflicting properties should be optimized, we proposed a methodology based on the modification of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The originally huge chemical search space is conveniently described by the Joback estimation method. The efficiency of the algorithm was enhanced by soft and hard structural constraints, which expedite the search for feasible molecules. These constraints are related to the number of available groups (fragments), the octet rule and the validity of the branches in the molecule. These constraints are also used to introduce a special genetic operator that improves the individuals of the populations to ensure the estimation of the properties is based on only reliable structures. The applicability of the proposed method is tested on several benchmark problems.
Minimization of off-grade production in multi-site multi-product plants by solving multiple traveling salesman problem
Continuous multi-product plants allow the production of several products (product grades). During grade transitions off-spec products are produced. The economic losses and the environmental impact of these transitions are sequence dependent, so the amount of off-grade products can be minimized by scheduling the sequence of the production of different products. Applying parallel production sites increases the flexibility of multi-product plants. Since market demands are changing, the production cycles of these sites should be re-scheduled in certain intervals. Therefore, our task is to design production cycles that contains all required products by minimizing the total length of grade transitions. Most production scheduling problems such as the one considered in this paper are NP-hard. Our goal is to solve realistic problem instances in no more than a couple of minutes. We show that this problem can be considered as a multiple traveling salesmen problem (mTSP), where the distances between the products are based on the time or costs of the grade transitions. The resulted mTSP has been solved by multi-chromosome based genetic algorithm. The proposed algorithm was implemented in MATLAB and is available at the website of the authors (Abonyi). For demonstration purposes, we present an illustrative example. The results show that multi-product multi-site scheduling problems can be effectively handled as mTSPs, and the proposed problem-specific representation based genetic algorithm can be used in wide range of optimization problems.
Redesign of the supply of mobile mechanics based on a novel genetic optimization algorithm using Google Maps API
If a mobile mechanic has to travel for material, productive time is lost. This paper presents a novel method to reduce activities regarding material handling with extending of serving locations. The design of the supply system can be considered as a complex combinatorial optimization problem, where the goal is to find a route plan with minimal route cost, which services all the demands from the central warehouses while satisfying the capacity and other constraints. We present a multi-chromosome technique for solving the multiple Traveling Salesman Problem (mTSP). The new operators based on a problem-specific representation proved to be more effective in terms of flexibility, complexity and transparency, and also in efficiency than the previous methods. The proposed optimization algorithm was implemented in MATLAB and integrated with Google Maps to provide a complete framework for distance calculation, definition of the initial routes, and visualization. This integrated framework was successfully applied in the solution of a real logistic problem, in the supply of mobile mechanics at one of Hungary׳s biggest energy providers.
Multi- and conflicting- objective process optimization problems can be effectively solved by interactive optimization
Process optimization problems often lead to multi-objective problems where optimization goals are non-commensurable and they are in conflict with each other. In such cases, the common approach, namely the application of a quantitative cost-function, may be very difficult or pointless. For these problems, we developed a method that handles these problems by introducing a human user into the evaluation procedure. Namely, the poposed method uses the expert knowledge directly in the optimization procedure. This approach has been applied successfully in computer graphics and engineering construction design, but it has not been used for chemical process engineering problems so far. During the development of the algorithm, we adopted this approach to typical process engineering problems. The results illustrate that the proposed tool offers a more flexible way to make a compromise among different goals than the conventional optimization methods do. The practical usefulness of the framework was demonstrated through two application examples: tuning of a multi-input multi-output controller and optimization of a fermentation process.
J. Madár, J. Abonyi, F. Szeifert, Interactive evolutionary computation in process engineering, Computers & Chemical Engineering, Volume 29, Issue 7, 15 June 2005, Pages 1591-1597, IF: 1.678 A MATLAB toolbox has been developed based on the proposed concept of Interactive Evolutionary Computation (example 1 and 2) , and it has more than 1500 users at the webpage of the MATLAB central file exchange (www.mathworks.com)
Interactive Evolutionary Computing
(EASY-IEC) MATLAB Toolbox
In some real-life optimization problems the objectives are often non-commensurable and are explicitly/mathematically not available. Interactive Evolutionary Computation (IEC) can effectively handle these problems.
Tamas Varga, Andras Kiraly, Janos Abonyi, Improvement of PSO algorithm by memory based gradient search - application in inventory management, Swarm Intelligence and Bio-inspired Computation, Elsevier, Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi, Mehmet Karamanoglu, pp.403-422
Genetic programming for model structure identification - genetic programming MATLAB toolbox
Linear-in-parameters models are quite widespread in process engineering, e.g. NAARX, polynomial ARMA models, etc. Genetic Programming (GP) is able to generate nonlinear input-output models of dynamical systems that are represented in a tree structure. This GP-OLS toolbox applies Orthogonal Least Squares algorithm (OLS) to estimate the contribution of the branches of the tree to the accuracy of the model. This method results in more robust and interpretable models than the classical GP method