An Improved Single Objective Optimization Approach for Double-Layered Multi-Head Weighing Process

We propose a chaotic evolution algorithm with elite strategy. The conventional chaotic evolution algorithm uses each individual to search in its local area. The proposed algorithm searches the parameter space always around the elite individual from the last generation. We evaluate the proposed algorithm in both single-objective and multi-objective optimization problems. In the single objective optimization problem, the elite is the individual has the best fitness value, and in the multi-objective optimization problem, the elites are the individuals in the first Pareto front. We design and evaluate these two algorithms with elite strategy using single- and multi-objective benchmark functions. We design a jump strategy to avoid searching within a local optima areas by applying elite strategy several generations one time. The numerical evaluation results demonstrate the proposed algorithm has strong local exploitation capability in the early generations. The optimization performance of chaotic evolution algorithm has a potential possibility to apply in high dimensional and more complex optimization problems.

IN practice, the mass of a neutral atom is expressed in terms of a particular nuclidic mass which is assumed to have an integral value and is termed the primary standard. With 12C as the primary standard (126M = 12u) nuclidic masses are found to be close to integral values and the difference between the measured mass (AZM) and the integral value Au is called the ‘mass excess’1. Alternatively, the mass of a nuclide (A, Z) relative to 12C could be expressed in the form: where Z(Δ)A12 is a small proper fraction (±) which will be referred to as the ‘mass fraction’. It should be noted that the ‘mass fraction’ is a dimensionless quantity whereas the ‘mass excess’ equals Z(Δ)A12 u.

In psychotic patients, altered hemodynamic responses and their significance in differential diagnosis are reported. However, the effects of antipsychotics and the associated increase of prolactin levels on prefrontal hemodynamic responses are not well understood. Antipsychotics may affect neurovascular coupling directly by inhibiting dopamine receptors1 or indirectly by increasing prolactin, which induces regional vasoconstriction.2 In a single-center observational study, we investigated the relation between prolactin levels and prefrontal hemodynamic responses. A total of 86 psychiatric patients, including 69 treated with antipsychotics, underwent near-infrared spectroscopy (NIRS) by a 22-channel topography system for diagnosis of psychotic disorders. We measured serum prolactin levels within a week from NIRS examination (Table S1). Changes of the concentration of oxygenated hemoglobin in prefrontal cortices were evaluated during a verbal fluency test, and three NIRS indices (integral value: size of area during the activation, centroid value: centroid time of area throughout, and initial value: axis assessed by an initial change) were analyzed against prolactin levels and chlorpromazine-equivalent doses by sex and cortex region. The ethics committee of Kusatsu Hospital approved this study, and informed consent to analyze the stacked data was obtained from all subjects. Serum prolactin levels were significantly negatively correlated with integral values and initial values at frontal and temporal regions in male subjects (Fig. S1), whereas, no such correlations were found in female subjects or between the chlorpromazine-equivalent doses and NIRS indices (Table S2). These results indicated that a higher prolactin level caused a decline in prefrontal hemodynamic responses in male subjects. The brain consumes high energy and requires adequate blood flow, which is facilitated by a tight coupling between neuronal activity and regional cerebral blood flow. Such neurovascular coupling is maintained by numerous mediators, such as nitric oxide, ion channels, and astrocytes.3 The prolactin receptor is expressed on astrocytes, which control various clinically relevant functions.4 Thus, activation of the prolactin receptor initiates multiple second-messenger cascades, which influence the vascular tone during tasks.4 Moreover, sex differences seemed to exist regarding the prolactin regulation on vascular tonus. Although dependent variables, such as psychiatric conditions and treatment durations, were not considered, our findings may suggest that prolactin levels relate to prefrontal hemodynamic responses in a psychiatric setting. Prolactin controls a vast number of physiological functions in the nervous system; however, its hemodynamic effect remains unclear. Further research to elucidate the effect of prolactin on cerebral blood flow via astrocyte prolactin receptor is needed. The authors declare no conflicts of interest. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques

In many recent works, the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize single-objective continuous optimization problems has been demonstrated. These numerical features provide the input for all kinds of machine learning tasks in the domain of continuous optimization problems, ranging, for example, from High-level Property Prediction to Automated Algorithm Selection and Automated Algorithm Configuration. Without ELA features, analyzing and understanding the characteristics of single-objective continuous optimization problems is-to the best of our knowledge-very limited. Yet, despite their usefulness, as demonstrated in several past works, ELA features suffer from several drawbacks. These include, in particular, (1) a strong correlation between multiple features, as well as (2) its very limited applicability to multiobjective continuous optimization problems. As a remedy, recent works proposed deep learning-based approaches as alternatives to ELA. In these works, among others, point-cloud transformers were used to characterize an optimization problem's fitness landscape. However, these approaches require a large amount of labeled training data. Within this work, we propose a hybrid approach, Deep-ELA, which combines (the benefits of) deep learning and ELA features. We pre-trained four transformers on millions of randomly generated optimization problems to learn deep representations of the landscapes of continuous single- and multiobjective optimization problems. Our proposed framework can either be used out of the box for analyzing single- and multiobjective continuous optimization problems, or subsequently fine-tuned to various tasks focusing on algorithm behavior and problem understanding.

A Multiobjective Optimization Problem (MOP) requires the optimization of several objective functions simultaneously, usually in conflict with each other. One of the most efficient algorithms for solving MOPs is MOEA/D (Multiobjective Evolutionary Algorithm Based on Decomposition), which decomposes a MOP into single-objective optimization subproblems and solves them using information from neighboring subproblems. MOEA/D variants with other evolutionary operators have emerged over the years, improving their efficiency in various MOPs. Recently, the IWOA (Improved Whale Optimization Algorithm) was proposed, an optimization algorithm bioinspired by the whale hunting method hybridized with Differential Evolution, which presented excellent results in single-objective optimization problems. This work proposes the MOEA/D-IWOA algorithm, which associates characteristics of the evolutionary operators of the IWOA to MOEA/D. Computational experiments were accomplished to analyze the performance of the MOEA/D-IWOA in benchmark MOPs suites. The results were compared with those obtained by the MOEA/D, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Third Evolution Step of Generalized Differential Evolution (GDE3), Improving the Strength Pareto Evolutionary Algorithm (SPEA2), and Indicator-Based Evolutionary Algorithm (IBEA) algorithms in the Hypervolume and Inverted Generational Distance Plus (IGD+) indicators. The MOEA/D-IWOA proved to be competitive, with a good performance profile, in addition to presenting the best results in some POMs.

In order to solve the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and fairness of users in cognitive radio network, based on quantum bee colony theory and membrane computing, a novel multi-objective discrete combinatorial optimization algorithm, named membrane-inspired quantum bee optimization, is proposed. The global optimal solution of single objective can be searched in the elementary membranes, and Pareto front solutions which take account of network utility and fairness, can be obtained from skin membrane with the proposed method. The multi-objective optimization algorithm, which can solve both single objective and multi-objective optimization problems at the same time, is designed by the communication rules between membranes, the cooperative evolution of foraging behavior based on quantum state, and non-dominated sorting. Compared with classical color-sensitive graph coloring algorithm, genetic algorithm, quantum genetic algorithm, and particle swarm optimization under different objective functions, the proposed spectrum allocation method can search the global optimal solution of single objective as shown by the simulation results, and it is superior to classical spectrum allocation algorithms and existing intelligence spectrum allocation methods. The optimal Pareto front solutions of multi-objective spectrum allocation are also obtained.

The Optimal Incorporation Distributed Generation (OIDG) problem can be treated as a single objective or multi-objective optimization problem. The advantage of the second option is to give a set of solutions in one run while optimizing more than one objective at the time. This paper aims to investigate the OIDG for single and multiple units in radial distribution systems using a Decomposition Based Multiobjective Evolutionary Algorithm. Seven case studies have been investigated considering three objectives which are active power losses minimization, voltage stability enhancement, and voltage profile improvement. All these case studies are tested on the 33-bus radial system.

Whale Optimization Algorithm (WOA) proposed by Seyedali Mirjalili and Andrew Lewis in 2016 is popular and powerful metaheuristic algorithm to search the global solution of optimization problems. WOA is nature-inspired, metaheuristic (randomization and deterministic) algorithm, which has been widely used to solve various single objective, multi objective and multidimensional optimization problems. WOA and its variant have been introduced in engineering applications, bioinformatics, multi-level image segmentation, clustering applications, design of low pass filter, Email classification, Diabetes classification, heterogeneous networks, machine learning etc. WOA is gradient free, easy to represent, capable to explore, exploit the search space and able to avoid local optima. This paper presents overview of WOA, its variants and applications. The performance of WOA is enhanced by introducing hybridization of other methods with WOA such as WOA-PSO, WOA-Levy, WOA-BAT, WOA-ANN, WOA-SVM etc. Objective of metaheuristic algorithm is tofind best position or leader position X* which is near to optimal solution for target prey over successive iteration. Objective function could be based on minimization or maximization approach.

This comment addresses the work of Wang and Li (DOI: 10.1007/s12293-009-0012-0, hereafter referred to as WL) published online on September 01, 2009 in a (special) issue of Memetic Computing. We respectfully beg to differ in opinion with WL and argue that the concept of multi-strategy evolutionary search used by WL is not particularly novel. Similar ideas have been presented elsewhere, and these publications date back to at least early 2007. For instance, in a series of papers published in PNAS (2007), SSSAJ (2008), WRR (2008), IEEE-TEVC (2009), JH (2010), Vrugt and coworkers have introduced AMALGAM, a multi-method (or ensemble) search approach to solve emerging single and multiple objective search and optimization problems. In the past few years, MATLAB, C++, Python, and R implementations of AMALGAM have been distributed extensively among researchers and practitioners in various fields of study.

Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective optimization may have in the single-objective space. For this purpose, we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems based on first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective domain and subsequently exploiting local structures of the resulting landscapes. Our study particularly focuses on the sensitivity of this multiobjectivization approach w.r.t. (1) the parametrization of the artificial second objective, as well as (2) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Nelder–Mead local search as optimizer in the respective module and compare the performance of the resulting hybrid local search to its original single-objective counterpart. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Hence, combined with more sophisticated local search and metaheuristics, this may help solve highly multimodal optimization problems in the future.

A theory of current–voltage characteristic (IVC) is proposed for a gated Corbino disk with a 2D spinless electron system and magnetic filling factors close to an integral value. The described results systematically include the features of the electrochemical potential of the 2D electron system in a magnetic field, which are responsible for sharp magnetocapacitance “dips” for integral values of the magnetic filling factor. The theory is used for qualitative interpretation of the observed weakly nonlinear peculiarities of IVC as well as for prediction of local details in the distribution of electron density and electric potential in a current-carrying Corbino disc. Corresponding measurements can be made, for instance, by using the linear electrooptical effect.

With the advent of Web 3.0 era, the number and complexity of Web pages in Bayesian networks have shown an explosive growth trend. Accompanying this is the geometric growth of information contained in Web pages. Web text data in Bayesian networks usually hide rich knowledge and rules of user value. However, due to the semi-structured, real-time and discrete characteristics of Web text data, it is difficult for users to obtain the knowledge they need directly from such complex data sets. The emergence of fuzzy mathematics provides a good research idea and method for solving such problems. It can use the idea of fuzzy mathematics to analyze the practical problems in text data. Therefore, how to effectively mine the Web text data information and knowledge that users really care about from Bayesian network, and present it in a way that users can understand, it is a very popular research topic at present. In this paper, we select the text of Bayesian network: microblog data for experiments. User data model of microblog is established by using relevant knowledge of fuzzy theory. The concept of fuzzy measure is introduced to calculate the non-additive measure value under the interaction relationship between the detection indicators. Determine the membership function relationship between the detection user and the text data, calculate the integral values of Choquet integral, Sugeno integral and Wang integral of the membership function under the non-additive measure, the final valuable web text data is judged by integral value. On the basis of the above research contents, the research results of Web text mining technology and fuzzy arithmetic mathematics are combined, design and implement information acquisition and analysis for Bayesian network community. The recall rate obtained by the experimental method in this paper is as low as 4%, and tends to be more stable.

Impairment of prefrontal cortex cognitive control functions in methamphetamine use disorder: A functional near-infrared spectroscopy study.

Application of Monte Carlo stochastic optimization (MOST) to deep learning