The problem of big data credibilistic fuzzy clustering is considered. This task was interested, when data are fed in both batch and online modes and has a lot of the global extremums. To find the global extremum of th...
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Nonlinear system identification and control is a very vast field. In order to make a brief review manageable the topic has been constrained to focus on systems with hysteresis. The model class that received most atten...
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Nonlinear system identification and control is a very vast field. In order to make a brief review manageable the topic has been constrained to focus on systems with hysteresis. The model class that received most attention was that of nonlinear autoregressive with exogenous (NARX) inputs polynomial models. However, most of the problems mentioned and tools presented are applicable to a much wider class of nonlinear systems. A framework for nonlinear system identification based on evolutionary algorithms is described. Identification and control of systems with hysteresis are illustrated with examples that include simulated and experimental data. Some open problems in the field are mentioned and it is hoped that this work will not only serve as a starting point for the newcomer but also motivate researchers to face open challenges.
This study aims to search for optimum design parameters for a slurry pipeline problem and optimum operation parameters for a multi-reservoir scheduling problem by using Bi-Attempted Base Optimization Algorithm (ABaOA)...
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This study aims to search for optimum design parameters for a slurry pipeline problem and optimum operation parameters for a multi-reservoir scheduling problem by using Bi-Attempted Base Optimization Algorithm (ABaOA), which has been recently developed as a numerical bidirectional search algorithm. The slurry pipeline problem is a constrained non-linear cost minimization problem with constraints on facility capacities. It has two separate cost terms that behave differently with changes in decision variables. The problem includes several decision variables in addition to the fact that the objective function is highly non-linear. On the other hand, the multi-reservoir problem is a well-known problem in Hydraulics that aims to maximize benefit by optimizing the releases of each reservoir. The problem has a known global optimum, which is used to test the abilities of the ABaOA. The ABaOA is developed from Base Optimization Algorithm (BaOA) by transforming its operators with the aim to diversify the search paths to reach the global optimum. Its applications in hydrosystems show that it converges to the optimum solutions in reasonable times. The results from the first application are compared to the ones obtained from Genetic algorithms (GA) application. It is observed that ABaOA outperformed GA in terms of speed of convergence and finding a better alternative solution. The ABaOA reaches the global optimum in the second application. In addition, alternatives with better benefit functions, including some penalties have been determined.
Recognizing critical nodes in complex networks has emerged as a challenging task across several application areas. The critical node detection problem (CNDP) is an optimization challenge that entails determining the s...
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Recognizing critical nodes in complex networks has emerged as a challenging task across several application areas. The critical node detection problem (CNDP) is an optimization challenge that entails determining the subset of nodes whose removal adversely affects network connectivity and performance based on certain predetermined criteria. The problem of recognizing critical nodes has received significant consideration since it is a vital challenge in a multitude of application areas. As a result, many variants have been proposed on the basis of numerous metrics. In this survey, we discuss different applications, challenges, and solutions to single- and multi-objective CNDP. We review and classify different recent advancements and obtained outcomes for each variant, proposed from 2017 to 2022. To our best knowledge, this is the first survey on the heuristic optimization-based solutions for CNDP that have been developed in recent years. This study also provides researchers with future insight into filling gaps in the critical nodes research field and identifying emerging research trends in this area.
Dynamic multi-objective optimization problems (DMOPs) have multiple objectives that need to be optimized simultaneously, while the objectives and/or constraints may change with time. Therefore, they require the solvin...
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Dynamic multi-objective optimization problems (DMOPs) have multiple objectives that need to be optimized simultaneously, while the objectives and/or constraints may change with time. Therefore, they require the solving algorithm to be able to properly converge to the Pareto optimal front and maintain the diversity of the population, and respond to environmental changes. Aiming at these points, a particle swarm optimization algorithm based on a double search strategy is proposed for dynamic multi-objective optimization in this paper. Two search strategies are designed to update the speed of each particle, which is helpful to accelerate the convergence speed and maintain the diversity of the population in a dynamic environment. In order to cope with environmental changes, an effective dynamic response mechanism is proposed, which is composed of an archive set prediction and piecewise search strategy to accelerate the convergence to the Pareto optimal set and maintain good distribution in the new environment. To verify the effectiveness of the proposed algorithm, it is tested on a series of benchmark problems and compared with several popular algorithms. The experimental results show the advantages of the proposed algorithm in dealing with DMOPs.
Analysis and design, as the most critical components in material science, require a highly rigorous approach to assure long-term success. Due to a recent increase in the amount of available experimental data, large da...
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Analysis and design, as the most critical components in material science, require a highly rigorous approach to assure long-term success. Due to a recent increase in the amount of available experimental data, large databases now contain a depth of knowledge on important properties of materials. The use of this information, combined with Machine Learning (ML) solutions, can enhance the materials' manufacturing process and efficiency. Indeed, ML can predict material properties, minimize the time and cost of laboratory testing, as well as optimize critical manufacturing processes. This paper aims to give an up-to-date review of the literature on how ML models are used to predict buildings' material properties (thermal, mechanical, and optical) and optimize the production lines for: a) Phase Change Materials (PCMs), b) Thermoelectric generators (TEGs), c) Customizable 3D-components, d) Advanced cement/concrete-based materials, e) Aerogels, f) Insulation components made from waste materials, g) Multifunctional component materials (MCs), h) Solar active building envelopes (SAE), i) Omniphobic coatings. The review showed that ML-driven approaches for materials' properties prediction in buildings and process optimization have grown rapidly, providing information and insights that can be utilized in the industry to maximize the materials' production and efficiency while reducing CO2 emissions, resulting in a more productive and environmentally friendly era.
The offspring selection strategy is the core of evolutionary algorithms, which directly affects the method's accuracy. Normally, to improve the search accuracy in local areas, the population converges quickly arou...
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The offspring selection strategy is the core of evolutionary algorithms, which directly affects the method's accuracy. Normally, to improve the search accuracy in local areas, the population converges quickly around the optimal individual. However, excessive aggregation can narrow the search range of the population, and thus the population may be trapped by local optima. To overcome this problem, a bare-bones particle swarm optimization with crossed memory (BPSO-CM) is proposed in this work. The BPSO-CM contains a multi-memory storage mechanism (MSM) and an elite offspring selection strategy (EOSS). The MSM enables an extra storage space to extend the search ability of the particle swarm and the EOSS enhances the local minimum escaping ability of the particle swarm. The population is endowed with the ability of enhanced global search through the cooperation of the MSM and the EOSS. To verify the performance of the BPSO-CM, the CEC2017 benchmark functions are used in experiments, five population-based methods are selected in the control group. Finally, experimental results proved that the BPSO-CM can present highly accurate results for global optimization problems.
Several tools for automatically testing Android applications have been proposed. In particular, Sapienz is a search-based tool that has been recently deployed in an industrial setting. Although it has been shown that ...
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Several tools for automatically testing Android applications have been proposed. In particular, Sapienz is a search-based tool that has been recently deployed in an industrial setting. Although it has been shown that Sapienz outperforms several state-of-the-art tools, it is still to be seen what features of SAPIENZ impact the most on its effectiveness. We conducted an extensive empirical study where we compare the impact of the search algorithm and the usage of motif genes, a more compact representation of individuals. Our empirical study shows that the usage of motif genes improves coverage both for evolutionary algorithms and random approaches. In particular, it also shows that NSGA-II, the multi-objective evolutionary algorithm used by Sapienz, does not have a clear improvement over other algorithms. In terms of number of crashes detected, our study shows that both NSGA-II and Random Search perform similarly. While the usage of motif genes improves the crash detection of algorithms, it is not enough to make it statistically significant. These facts cast doubts about the use of evolutionary algorithms in the context of Android test generation and suggest that motif genes can have a great impact on the overall effectiveness.
Problems with multiple interdependent components offer a better representation of the real-world situations where globally optimal solutions are preferred over optimal solutions for the individual components. One such...
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Problems with multiple interdependent components offer a better representation of the real-world situations where globally optimal solutions are preferred over optimal solutions for the individual components. One such model is the Travelling Thief Problem (TTP);while it may offer a better benchmarking alternative to the standard models, only one form of inter-component dependency is investigated. The goal of this paper is to study the impact of different models of dependency on the fitness landscape using performance prediction models (regression analysis). To conduct the analysis, we consider a generalised model of the TTP, where the dependencies between the two components of the problem are tunable through problem features. We use regression trees to predict the instance difficulty using an efficient memetic algorithm that is agnostic to the domain knowledge to avoid any bias. We report all the decision trees resulting from the regression model, which is the core in understanding the relationship between the dependencies (represented by the features) and problem difficulty (represented by the runtime). The regression model was able to predict the expected runtime of the algorithm based on the problem features. Furthermore, the results show that the contribution of the item value drop dependency is significantly higher than the velocity change dependency.
A computational technique called opposition-based learning (OBL) has gained a lot of attention in the field of soft computing. Among the different forms of OBL, the iBetaCOBL variant, which is a stochastic OBL, has sh...
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A computational technique called opposition-based learning (OBL) has gained a lot of attention in the field of soft computing. Among the different forms of OBL, the iBetaCOBL variant, which is a stochastic OBL, has shown the best results in improving the performance of differential evolution (DE). However, this approach may not be effective in handling complex problems as it is susceptible to rotations in the coordinate system. To address this issue, we present a new and improved version of iBetaCOBL called iBetaCOBL-eig. This new technique uses an eigenvector-based multiple exponential crossover operator in the partial dimensional change method, making it rotationally invariant. We conducted experiments to evaluate the performance enhancements of DE using iBetaCOBL-eig on 29 challenging benchmark problems. Our results showed that the new algorithm is able to outperform not only ten strong OBL variants but also its previous version iBetaCOBL.
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