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This paper proposes an improved hybrid method to compute the parameters of the two-diode model of photovoltaic (PV) module. Unlike previous methods, it attains the speed of the analytical approach by utilizing only datasheet information. Furthermore, its accuracy is not compromised as it does not require simplifications in its computation. Four parameters are determined analytically, while the remaining three are optimized by using an evolutionary algorithm, i.e., the differential evolution. The speed is improved because the parameters are optimized only once, i.e., at standard test condition, while the values at other conditions are computed analytically. Furthermore, a procedure to guide the initial conditions of the Newton–Raphson iteration is introduced. For validation, the algorithm is compared to other established computational methods for mono-, polycrystalline, and thin film modules. When evaluated against the experimental data, the mean absolute error is improved by one order of magnitude, while the speed is increased by approximately threefold. The standard deviation of the decision parameters over 100 independent runs is less than 0.1—which suggests that the optimization process is very consistent. Due to its speed and accuracy, the method is envisaged to be useful as a computational engine in PV simulator. [ABSTRACT FROM PUBLISHER]

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method. [ABSTRACT FROM PUBLISHER]

We propose in this paper to consider code-smells detection as a distributed optimization problem. The idea is that different methods are combined in parallel during the optimization process to find a consensus regarding the detection of code-smells. To this end, we used Parallel Evolutionary algorithms (P-EA) where many evolutionary algorithms with different adaptations (fitness functions, solution representations, and change operators) are executed, in a parallel cooperative manner, to solve a common goal which is the detection of code-smells. An empirical evaluation to compare the implementation of our cooperative P-EA approach with random search, two single population-based approaches and two code-smells detection techniques that are not based on meta-heuristics search. The statistical analysis of the obtained results provides evidence to support the claim that cooperative P-EA is more efficient and effective than state of the art detection approaches based on a benchmark of nine large open source systems where more than 85 percent of precision and recall scores are obtained on a variety of eight different types of code-smells. [ABSTRACT FROM PUBLISHER]