An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice

Population-Based Optimization Algorithms for Solving the Travelling Salesman Problem

[Extract] Population based optimization algorithms are the techniques which are in the set of the nature based optimization algorithms. The creatures and natural systems which are working and developing in nature are one of the interesting and valuable sources of inspiration for designing and inventing new systems and algorithms in different fields of science and technology. Evolutionary Computation (Eiben& Smith, 2003), Neural Networks (Haykin, 99), Time Adaptive Self-Organizing Maps (Shah-Hosseini, 2006), Ant Systems (Dorigo & Stutzle, 2004), Particle Swarm Optimization (Eberhart & Kennedy, 1995), Simulated Annealing (Kirkpatrik, 1984), Bee Colony Optimization (Teodorovic et al., 2006) and DNA Computing (Adleman, 1994) are among the problem solving techniques inspired from observing nature. In this chapter population based optimization algorithms have been introduced. Some of these algorithms were mentioned above. Other algorithms are Intelligent Water Drops (IWD) algorithm (Shah-Hosseini, 2007), Artificial Immune Systems (AIS) (Dasgupta, 1999) and Electromagnetism-like Mechanisms (EM) (Birbil & Fang, 2003). In this chapter, every section briefly introduces one of these population based optimization algorithms and applies them for solving the TSP. Also, we try to note the important points of each algorithm and every point we contribute to these algorithms has been stated. Section nine shows experimental results based on the algorithms introduced in previous sections which are implemented to solve different problems of the TSP using well-known datasets

Generalized Hopfield networks for constrained optimization

A twofold generalization of the classical continuous Hopfield neural network for modelling constrained optimization problems is proposed. On the one hand, non-quadratic cost functions are admitted corresponding to non-linear output summation functions in the neurons. On the other hand it is shown under which conditions various (new) types of constraints can be incorporated directly. The stability properties of several relaxation schemes are shown. If a direct incorporation of the constraints appears to be impossible, the Hopfield-Lagrange model can be applied, the stability properties of which are analyzed as well. Another good way to deal with constraints is by means of dynamic penalty terms, using mean field annealing in order to end up in a feasible solution. A famous example in this context is the elastic net, although it seems impossible - contrary to what is suggested in the literature - to derive the architecture of this network from a constrained Hopfield model. Furthermore, a non-equidistant elastic net is proposed and its stability properties are compared to those of the classical elastic network. In addition to certain simulation results as known from the literature, most theoretical statements of this paper are validated with simulations of toy problems while in some cases, more sophisticated combinatorial optimization problems have been tried as well. In the final section, we discuss the possibilities of applying the various models in the area of constrained optimization. It is also demonstrated how the new ideas as inspired by the analysis of generalized continuous Hopfield models, can be transferred to discrete stochastic Hopfield models. By doing so, simulating annealing can be exploited in other to improve the quality of solutions. The transfer also opens new avenues for continued theoretical research

An On-demand Photonic Ising Machine with Simplified Hamiltonian Calculation by Phase-encoding and Intensity Detection

Photonic Ising machine is a new paradigm of optical computing, which is based on the characteristics of light wave propagation, parallel processing and low loss transmission. Thus, the process of solving the combinatorial optimization problems can be accelerated through photonic/optoelectronic devices. In this work, we have proposed and demonstrated the so-called Phase-Encoding and Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems on demand. The PEIDIA is based on the simulated annealing algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase term of the optical field and only intensity detection is required during the solving process. As a proof of principle, several 20 and 30-dimensional Ising problems have been solved with high ground state probability

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA

Cross‐entropy method for distribution power systems reconfiguration

Cross-entropy (CE) is a powerful simulation method for the solution of continuous and combinatory optimization problems. The work presented here utilizes the CE method for the optimal topology of distribution power systems (DPSs). The optimal network switches are determined for the reduction of active power loss. The adapted CE method is tested on three case studies, namely, the 33-node, 83-node, and 880-node DPSs. The results are compared with other reconfigura-tion algorithms to demonstrate the superiority of the proposed algorithm. The impact of the distributed generation is also investigated. The effective integration of the photovoltaic panels at midday, when their production is highest and meets the peak demand, is showed. Finally, the real-time reconfiguration strategy based on the switching effort reduction is proposed and enhanced via an adequate selection of the initial switch states

Computational optimization of transcatheter aortic valve leaflet design

Transcatheter aortic valve (TAV) replacement is now the standard of care treatment for aortic stenosis in high-risk patients. There has been a recent push in the industry to develop smaller profile TAVs to make this treatment a safe and effective alternative to valve surgery in an even wider spectrum of patients. Smaller devices requiring thinner leaflets may come with the tradeoff of reduced durability. However, the impacts of different TAV leaflet materials and valve designs on TAV function and durability, particularly under non-ideal deployment conditions, have not been thoroughly assessed. By combining material modeling and geometric parameterization of valve leaflets, performance and safety of TAV devices can be comprehensively evaluated. The objectives of this study were to employ constitutive modeling tools to describe the material properties of pericardial tissues, and then to implement them in the development of a computational framework for exploring the impacts of leaflet material and design on TAV function. The mechanical properties of pericardial tissue have not been well characterized, particularly under flexure, which is an important mode of deformation for native and bioprosthetic heart valves. Pericardia material models were implemented in finite element simulations of valve deformation and directly compared. The impact of TAV leaflet material and geometry on mechanical stress was closely examined and fundamental relationships between design characteristics and leaflet deformation were established. Eccentric TAV expansion was modeled and optimization tools were employed to identify leaflet geometries which minimize stress during mechanical loading to increase durability. The results from these studies may offer scientific rationale for the design of durable and robust next-generation TAV devices.M.S

Metodologia Per la Caratterizzazione di amplificatori a basso rumore per UMTS

In questo lavoro si presenta una metodologia di progettazione elettronica a livello di sistema, affrontando il problema della caratterizzazione dello spazio di progetto dell' amplificatore a basso rumore costituente il primo stadio di un front end a conversione diretta per UMTS realizzato in tecnologia CMOS con lunghezza di canale .18u. La metodologia è sviluppata al fine di valutare in modo quantititativo le specifiche ottime di sistema per il front-end stesso e si basa sul concetto di Piattaforma Analogica, che prevede la costruzione di un modello di prestazioni per il blocco analogico basato su campionamento statistico di indici di prestazioni del blocco stesso, misurati tramite simulazione di dimensionamenti dei componenti attivi e passivi soddisfacenti un set di equazioni specifico della topologia circuitale. Gli indici di prestazioni vengono successivamente ulizzati per parametrizzare modelli comportamentali utilizzati nelle fasi di ottimizzazione a livello di sistema. Modelli comportamentali atti a rappresentare i sistemi RF sono stati pertanto studiati per ottimizzare la scelta delle metriche di prestazioni. L'ottimizzazione dei set di equazioni atti a selezionare le configurazione di interesse per il campionamento ha al tempo stesso richiesto l'approfondimento dei modelli di dispositivi attivi validi in tutte le regioni di funzionamento, e lo studio dettagliato della progettazione degli amplificatori a basso rumore basati su degenerazione induttiva. Inoltre, il problema della modellizzazione a livello di sistema degli effetti della comunicazione tra LNA e Mixer è stato affrontato proponendo e analizzando diverse soluzioni. Il lavoro ha permesso di condurre un'ottimizzazione del front-end UMTS, giungendo a specifiche ottime a livello di sistema per l'amplificatore stesso

Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topi