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How to design an optimal mixed H2/H∞ robust FID controller for a complex control system is of great practical importance, but it is still an open issue. From the perspective of evolutionary algorithm, this paper formulates this issue firstly as a typical constrained optimization problem by minimizing a weighted objective function consisting of the robust stability performance, disturbance attenuation...
Designing complex system architectures involves analysing tradeoffs between multiple conflicting decision criteria to find a solution which best matches the preferences of the customer. This is usually done in the engineering characteristic (decision criteria) space, but the customer is generally more interested in higher-level characteristics. For example, the engineering characteristic "modularity"...
Compact evolutionary algorithms (cEAs) are optimization algorithms that require minimal computational cost. They do not require the storage of the population but they represent it by a distribution function. In all known cEAs, normal probability of density function (N-PDF) is used. In this paper, in order to improve the performance of cEAs and to reduce their complicity, we propose a more simple distribution...
Ever-increasing big data forces enterprises to migrate data to cloud storage systems. Data retrieval time from the cloud will directly affect the overall application performance. Meanwhile, sensitive data stored on cloud necessitates a robust security arrangement against cyberattacks. Therefore, it is imperative that both data retrieval time and data security should be taken into account simultaneously...
The cooperative co-evolution (CC) framework is one of the most efficient methods to solve large scale optimization problems. The traditional CC framework divides decision variables into several mutually-exclusive groups. In this paper, we propose the overlapped cooperative co-evolution (OCC) framework for large scale optimization problems. In OCC framework, the decision variables that have strong...
A simple hydropower optimization problem is used to compare the computational efficiency of the Honey Bees Mating Optimization Algorithm (HBMOA), with the efficiency of other evolutionary algorithms, namely 3 recent ones: firefly algorithm, cuckoo search algorithm and bat-inspired algorithm (BA). The selected case study is a hydropower development on the Arges river, in Romania, consisting of Vidraru...
The optimization of a microwave circuit is a complex multi-objective problem (MOP) needed to be effectively solved. Multi-objective evolution algorithms (MOEAs) are efficient in dealing with MOPs because of their population property inspired by the natural evolution of species. Exploitation and exploration are of equal importance to MOEAs for approximating the optimal Pareto front (PF) well. However,...
Evolutionary algorithms are optimization methods inspired by natural evolution. They usually search for the optimal solution in large space areas. In Evolutionary Algorithms it is very important to select an appropriate balance between the ability of the algorithm to explore and exploit the search space. The paper presents a hybrid system consisting of a Genetic Algorithm and an Evolutionary Strategy...
The hyper-parameter optimization of machine learning model is not a completely solved problem. The exquisite combination of artificial tuning and grid search may be a good choice in the area where the dimension of hyper-parameters is very low. But for high-dimensional hyper-parameter optimization problems, artificial tuning and grid search are obviously helpless. In this paper, we propose a quantum...
Image match has been widely used in computer vision, pattern recognition and image processing. The matching efficiency is a focus topic in the field and some methods have been presented, such as simplification of similarity measure, application of optimization algorithms. Particle swarm optimization algorithm (PSO) has been utilized successfully for image match. However, it is easy to fall into the...
In Search based Software Engineering, well-known evolutionary algorithms are utilized to find the optimal solutions and address the configuration optimization problem for software product lines and trade off multiple often competing objectives. Previous work by Henard et al. showed the weakness of the constraint expressiveness and the optimality and speed. In this work, we propose a multi-objective...
Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness...
In this paper, a variant of the recently introduced whale optimization algorithm (WOA) was proposed based on adaptive switching of random walk per individual search agent. WOA is recently proposed bio-inspired optimizers that employ two different random walks. The original optimizer stochastically switches between the two random walk at each iteration regardless of the search agents performance and...
Regression testing is the testing activity performed after changes occurred on software. Its aim is to increase confidence that achieved software adjustments have no negative impact on the already functional parts of the software. Test case prioritization is one technique that could be applied in regression testing with the aim to find faults early, resulting in reduced cost and shorten time of testing...
The traveling salesman problem (TSP) is one of the most classical combinatorial optimization problems and has attracted a lot of interests from researchers. Many studies have proposed various methods for solving the TSP. Biogeography-based optimization (BBO) is a novel evolutionary algorithm based on migration and mutation mechanism of species between the islands in biogeography. In this paper, we...
This paper addresses the problem of Data Centers (DC) energy efficiency by proposing a proactive optimization technique to schedule the day-ahead DC operation to minimize the operational cost. The proactive optimization technique is formalized as a Mixed Integer Optimal Control Problem, known to be NP-hard. Because the time needed for solving this problem by some of the gradient-based solvers depends...
Modern communication technology demands high performance miniaturized planar antennas. Designing planar antennas such as microstrip patch are well studied and have tremendous scope in the development based on the required objectives. Several optimization techniques were reported for the design of patch antennas but limited to dimensional parameters only. In this work we have adapted evolutionary algorithm...
Nowadays, Peer-to-Peer computing technology (P2P) is widely used on Internet, which has brought great challenges to effective management of the network. As a result, it is very important to recognize P2P applications as to maintain network. In essence, to identify traffic of P2P is a problem belongs to pattern recognition. As one of the optimal classifiers, support vector machine (SVM) has special...
Popular topics in current research within the games community are general game playing and general video game playing. Both of these efforts seek to find relatively general purpose AI to play games. Within the optimization community we are approaching the 20th anniversary of the no free lunch theorem. In this paper we suggest reasons why a games version of a no free lunch result is probably not problematic...
The joint optimization of partial response continuous phase modulation (CPM) parameters is addressed. For fixed modulation order and memory length, the modulation index, the symbol rate and the phase pulse shape are considered as degrees of freedom. In particular, we drop the restriction of “classical” phase pulses and we allow for custom shapes, which are parametrized through Bezier curves. In order...
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