In recent years, researchers have taken the many-objective optimization algorithm, which can optimize 5, 8, 10, 15, 20 objective functions simultaneously, as a new research topic. However, the ...current research on many-objective optimization technology also encounters some challenges. For example: Pareto resistance phenomenon, difficult diversity maintenance. Based on the above problems, this paper proposes a many-objective evolutionary algorithm based on three states (MOEA/TS). Firstly, a feature extraction operator is proposed. It can extract the features of the high-quality solution set, and then assist the evolution of the current individual. Secondly, based on Pareto front layer, the concept of “individual importance degree” is proposed. The importance degree of an individual can reflect the importance of the individual in the same Pareto front layer, so as to further distinguish the advantages and disadvantages of different individuals in the same front layer. Then, a repulsion field method is proposed. The diversity of the population in the objective space is maintained by the repulsion field, so that the population can be evenly distributed on the real Pareto front. Finally, a new concurrent algorithm framework is designed. In the algorithm framework, the algorithm is divided into three states, and each state focuses on a specific task. The population can switch freely among these three states according to its own evolution. The MOEA/TS algorithm is compared with 7 advanced many-objective optimization algorithms. The experimental results show that the MOEA/TS algorithm is more competitive in many-objective optimization problems.
The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle ...impoverishment problem, which can lead to the misleading state estimation results. To cope with this problem, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed in this paper. It is inspired from the genetic algorithm. The particle impoverishment in general PF mainly results from the poverty of particle diversity. In IPF, the genetic-operators-based strategy is designed to further improve the particle diversity. It should be pointed out that the general PF is a special case of the proposed IPF with the specified parameters. Two experiment examples show that IPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. Finally, the proposed IPF is implemented for real-time fault detection on a three-tank system, and the results are satisfactory.
This paper presents a novel, optimization algorithm called Equilibrium Optimizer (EO), inspired by control volume mass balance models used to estimate both dynamic and equilibrium states. In EO, each ...particle (solution) with its concentration (position) acts as a search agent. The search agents randomly update their concentration with respect to best-so-far solutions, namely equilibrium candidates, to finally reach to the equilibrium state (optimal result). A well-defined “generation rate” term is proved to invigorate EO’s ability in exploration, exploitation, and local minima avoidance. The proposed algorithm is benchmarked with 58 unimodal, multimodal, and composition functions and three engineering application problems. Results of EO are compared to three categories of existing optimization methods, including: (i) the most well-known meta-heuristics, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO); (ii) recently developed algorithms, including Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), and Salp Swarm Algorithm (SSA); and (iii) high performance optimizers, including CMA-ES, SHADE, and LSHADE-SPACMA. Using average rank of Friedman test, for all 58 mathematical functions EO is able to outperform PSO, GWO, GA, GSA, SSA, and CMA-ES by 60%, 69%, 94%, 96%, 77%, and 64%, respectively, while it is outperformed by SHADE and LSHADE-SPACMA by 24% and 27%, respectively. The Bonferroni–Dunnand Holm’s tests for all functions showed that EO is significantly a better algorithm than PSO, GWO, GA, GSA, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-SPACMA. The source code of EO is publicly availabe at https://github.com/afshinfaramarzi/Equilibrium-Optimizer, http://built-envi.com/portfolio/equilibrium-optimizer/ and http://www.alimirjalili.com/SourceCodes/EOcode.zip.
•Developed a novel optimization algorithm inspired by mass balance models.•Tested EO against well-studied mathematical and engineering benchmarks.•Compared the algorithm to other well-known meta-heuristics.•Demonstrated effectiveness and superiority of the proposed method.
Evolutionary paradigms have been successfully applied to neural network designs for two decades. Unfortunately, these methods cannot scale well to the modern deep neural networks due to the ...complicated architectures and large quantities of connection weights. In this paper, we propose a new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems. In the proposed algorithm, an efficient variable-length gene encoding strategy is designed to represent the different building blocks and the potentially optimal depth in convolutional neural networks. In addition, a new representation scheme is developed for effectively initializing connection weights of deep convolutional neural networks, which is expected to avoid networks getting stuck into local minimum that is typically a major issue in the backward gradient-based optimization. Furthermore, a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource. The proposed algorithm is examined and compared with 22 existing algorithms on nine widely used image classification tasks, including the state-of-the-art methods. The experimental results demonstrate the remarkable superiority of the proposed algorithm over the state-of-the-art designs in terms of classification error rate and the number of parameters (weights).
In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the ...new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.
Variations of produced power in windmills may influence the appropriate integration in power-driven grids which may disrupt the balance between electricity demand and its production. Consequently, ...accurate prediction is extremely preferred for planning reliable and effective execution of power systems and to guarantee the continuous supply. For this purpose, a novel genetic long short term memory (GLSTM) framework comprising of long short term memory and genetic algorithm (GA) is proposed to predict short-term wind power. In the proposed GLSTM model, the strength of LSTM is employed due to its capability of automatically learning features from sequential data, while the global optimization strategy of GA is exploited to optimize window size and number of neurons in LSTM layers. Prediction from GLSTM has been compared with actual power, predictions of support vector regressor, and with reported techniques in terms of standard performance indices. It can be evaluated from the comparison that GLSTM and its variants provide accurate, reliable, and robust predictions of wind power of seven wind farms in Europe. In terms of percentage improvement, GLSTM, on average, improves wind power predictions from 6% to 30% as opposed to existing techniques. Wilcoxon signed-rank test demonstrates that GLSTM is significantly different from standard LSTM.
•LSTM with ability of learning features from sequence data employed on wind dataset.•Genetic algorithm optimizes window size and number of neurons in LSTM layers.•Novel genetic LSTM (GLSTM) is proposed as LSTM with for wind power forecast.•Compared error measures from GLSTM with existing techniques and LSTM.•Wilcoxon Signed-Rank test ensures that GLSTM is significantly different from LSTM.
This paper presents a stochastic framework for day-ahead scheduling of microgrid energy storage systems in the context of multi-objective (MO) optimization. Operation cost of microgrid in normal ...conditions and load curtailment index in case of unscheduled islanding events (initiated by disturbances in the main grid) are chosen as main criteria of the proposed scheme. In practice, duration of disconnection from the upstream network is unknown in unscheduled islanding incidents and cannot be predicted with certainty. To properly handle the uncertainties associated with time and duration of such events as well as microgrid load and renewable power generation, stochastic models are involved in the MO scheduling framework and they are formulated as mixed integer linear programming problems. The non-dominated sorting genetic algorithm II is employed to effectively cope with the MO optimization problem and a fuzzy decision making approach is employed for appropriate representation of microgrid operator's preferences in compromising between the two objectives. The proposed scheme is implemented on a test microgrid and the obtained results demonstrate the applicability and efficiency of this framework in dealing with conflicting requirements of microgrid security and economic operation.
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