Grey Wolf Optimizer-Based Approach to the Tuning of Pi-Fuzzy Controllers with a Reduced Process Parametric Sensitivity

Nash Equilibrium Seeking for Graphic Games With Dynamic Event-Triggered Mechanism

In this article, a discrete-time Nash equilibrium (NE) seeking problem is studied for a class of graphic games. In order to reduce the signal transmission frequency between adjacent players, a dynamic event-triggered mechanism is designed. For the purpose of regulating the actions of players to the NE points, a discrete-time NE seeking strategy is designed by only using the local action information. Then, sufficient conditions are provided to ensure that the actions of all players converge to the NE point. Finally, a numerical example of a multisatellite communication coordination problem is given to verify the effectiveness of the proposed NE seeking method.

A multilayer affective computing model with evolutionary strategies reflecting decision-makers' preferences in process control

Many industrial control problems related to multi-objective optimization, such as controller parameters tuning, often require operators to perform multiple-step interactions without considering the changes of decision-makers' affective states and quantitative description of decision-makers' preferences during the interactive decision. Regarding this problem, we developed a multilayer affective computing model (MACM), including three factors: human personality, emotional space, and affective states, to demonstrate the iterative affective computing during the interactions. First, a concise model of affective computing-driven interactive decision-making was built before three submodules involved were described in detail. (1) An affective state recognition method based on facial expressions was presented, providing the basis for obtaining expert affective states during decision-making. (2) An identification method of affective parameters was given, providing an approach to describing personalized affective state-changing rules of different persons. (3) A definition of decision-makers' preferences in interactive decision-making was specified. In addition, a decision-makers' preferences mining method was developed by the MACM and an iterative learning control (ILC) strategy. Thus, we proposed affective computing-driven interactive decision-making method, which provided a simplified approach to converting the interactive decision problems based on decision-makers' preferences to decision issues based on incremental decision vector, along with assisting computers to learn from human experts and perform decision-making automatically in a general sense. Then, two typical process control cases-PI controller tuning for decoupling problem and manipulate vector optimization for batch processes were used to show the correctness and effectiveness of the contributions. Compared with other traditional optimization algorithms without affective state tracking and recognition (fuzzy control, ILC, reinforcement learning, and so on), experimental results indicated that the proposed method could achieve good performance. Finally, this study presented the efficiency and limitations of using this technique for a specific application.

A parametric approach to fuzzy multi-objective linear fractional program: An alpha cut based method

In the multi-objective programming problem (MOPP), finding an efficient solution is challenging and partially encompasses some difficulties in practice. This paper presents an approach to address the multi-objective linear fractional programing problem with fuzzy coefficients (FMOLFPP). In the method, at first, the concept of α - cuts is used to change the fuzzy numbers into intervals. Therefore, the fuzzy problem is further changed into an interval-valued linear fractional programming problem (IVLFPP). Afterward, this problem is transformed into a linear programming problem (LPP) using a parametric approach and the weighted sum method. It is proven that the solution resulted from the LPP is at least a weakly ɛ - efficient solution. Two examples are given to illustrate the method.

Chaos-opposition-enhanced slime mould algorithm for minimizing the cost of energy for the wind turbines on high-altitude sites

This paper presents a chaos-opposition-enhanced slime mould algorithm (CO-SMA) to minimize energy (COE) cost for the wind turbines on high-altitude sites. The COE model is established based on rotor radius, rated power, and hub height needed to achieve an optimal design model. In this context, an improved variant of SMA, named CO-SMA, is proposed based on a chaotic search strategy (CSS) and crossover-opposition strategy (COS) to cope with the potential weaknesses classical SMA while dealing with nonlinear tasks. First, the COS is introduced to enhance the diversity of solutions and thus improves the exploratory tendencies. The CSS is incorporated into the basic SMA to improve the exploitative abilities and thus avoids the premature convergence dilemma. The proposed CO-SMA is validated on the design of wind turbines with high-altitude sites. Furthermore, the sensitivity analysis based on the Taguchi method is developed to exhibit the impact of the COE model's optimized parameters. The influence of uncertainty based on the fuzziness scheme of wind resource statistics is also explored to depict a real scheme for the changes that occurred by seasonal time, atmospheric conditions, and topographic conditions. The proposed CO-SMA is compared with the PSO, WOA, GWO, MDWA, and SMA, where the COE values are recorded as 0.052408, 0.052462, 0.052435, 0.052409, 0.052413, and 0.052915, respectively. Furthermore, the proposed CO-SMA records the faster convergence than the others. On the other hand, the Taguchi method reveals that the rated power is the most significant parameter on the COE model. Also, the impact of the fuzziness scheme on COE is exhibited, where the increasing interval of vagueness can increase the value of COE.

Efficient Nonlinear Model Predictive Control for Quadrotor Trajectory Tracking: Algorithms and Experiment

This article studies an efficient nonlinear model-predictive control (NMPC) scheme for trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV). By augmenting the desired trajectory to a reference dynamical system, we can make the tracking task fit into the standard NMPC framework. In order to alleviate the heavy computational burden caused by solving the corresponding NMPC optimization problem online, we develop an improved continuation/generalized minimal residual ( [Formula: see text]/GMRES) algorithm. Compared with the standard C/GMRES method, the inequality constraint is relaxed by imposing the penalty term on the cost function. To guarantee the closed-loop system stability, we introduce a contraction constraint. Based on the proposed numerical algorithm and the stability constraint, we develop a novel efficient-NMPC algorithm to achieve acceptable control performance with reduced computational complexity. The numerical convergence of [Formula: see text]/GMRES solutions and the closed-loop stability of efficient-NMPC are theoretically analyzed in the presence of the input constraint. Finally, the numerical simulations, software-in-the-loop (SIL) simulations, and the real-time experiment are given to demonstrate the effectiveness of the proposed [Formula: see text]/GMRES algorithm and efficient-NMPC scheme.

AOIO: Application Oriented I/O Optimization for Buffer Management

Emerging scale-out I/O intensive applications are broadly used now, which process a large amount of data in buffer/cache for reorganization or analysis and their performances are greatly affected by the speed of the I/O system. Efficient management scheme of the limited kernel buffer plays a key role in improving I/O system performance, such as caching hinted data for reuse in future, prefetching hinted data, and expelling data not to be accessed again from a buffer, which are called proactive mechanisms in buffer management. However, most of the existing buffer management schemes cannot identify data reference regularities (i.e., sequential or looping patterns) that can benefit proactive mechanisms, and they also cannot perform in the application level for managing specified applications. In this paper, we present an A pplication Oriented I/O Optimization (AOIO) technique automatically benefiting the kernel buffer/cache by exploring the I/O regularities of applications based on program counter technique. In our design, the input/output data and the looping pattern are in strict symmetry. According to AOIO, each application can provide more appropriate predictions to operating system which achieve significantly better accuracy than other buffer management schemes. The trace-driven simulation experiment results show that the hit ratios are improved by an average of 25.9% and the execution times are reduced by as much as 20.2% compared to other schemes for the workloads we used.

Optimization of Demand-Response-Based Intelligent Home Energy Management System with Binary Backtracking Search Algorithm

In many nations, limited power from providers and an increase in demand for electricity have created new opportunities that can be used by home energy management systems (HEMSs) systems to enforce proper use of energy. This paper presents a virtual intelligent home with demand response (DR) model home appliances that have an inverter air conditioner, water pump, washing machine, and inverter refrigerator. A binary backtracking search algorithm (BBSA) is proposed to introduce the optimal schedule controller. With the proposed BBSA schedule controller, the highest energy consumption during DR can be reduced by 33.84% during the weekends and by 30.4% daily during the weekdays. The results indicate the effectiveness of the proposed HEMS. Additionally, the model can control the appliances and maintain total residential energy consumption below the defined demand limit.

A bi-objective home healthcare routing and scheduling problem considering patients' satisfaction in a fuzzy environment

Home care services are an alternative answer to hospitalization, and play an important role in reducing the healthcare costs for governments and healthcare practitioners. To find a valid plan for these services, an optimization problem called the home healthcare routing and scheduling problem is motivated to perform the logistics of the home care services. Although most studies mainly focus on minimizing the total cost of logistics activities, no study, as far as we know, has treated the patients' satisfaction as an objective function under uncertainty. To make this problem more practical, this study proposes a bi-objective optimization methodology to model a multi-period and multi-depot home healthcare routing and scheduling problem in a fuzzy environment. With regards to a group of uncertain parameters such as the time of travel and services as well as patients' satisfaction, a fuzzy approach named as the Jimenez's method, is also utilized. To address the proposed home healthcare problem, new and well-established metaheuristics are obtained. Although the social engineering optimizer (SEO) has been applied to several optimization problems, it has not yet been applied in the healthcare routing and scheduling area. Another innovation is to develop a new modified multi-objective version of SEO by using an adaptive memory strategy, so-called AMSEO. Finally, a comprehensive discussion is provided by comparing the algorithms based on multi-objective metrics and sensitivity analyses. The practicality and efficiency of the AMSEO in this context lends weight to the development and application of the approach more broadly.

An Improved Bacterial Foraging Optimization with Differential and Poisson Distribution Strategy and its Application to Nurse Scheduling Problem

Bacterial Foraging Optimization (BFO) has been predominately applied to some real-world problems, but this method has poor convergence speed over complex optimization problems. In this paper, an improved Bacterial Foraging Optimization with Differential and Poisson Distribution strategies (PDBFO) is proposed to promote the insufficiency of BFO. In PDBFO, the step size of bacteria is segmented and adjusted in accordance with fitness value to accelerate convergence and enhance the search capability. Moreover, the differential operator and the Poisson Distribution strategy are incorporated to enrich individual diversity, which prevents algorithm from being trapped in the local optimum. Experimental simulations on eleven benchmark functions demonstrate that the proposed PDBFO has better convergence behavior in comparison to other six algorithms. Additionally, to verify the effectiveness of the method in solving the real-world complex problems, the PDBFO is also applied to the Nurse Scheduling Problem (NSP). Results indicate that the proposed PDBFO is more effective in obtaining the optimal solutions by comparing with other algorithms.

Multi-objective Combinatorial Generative Adversarial Optimization and Its Application in Crowdsensing

With the increasing of the decision variables in multi-objective combinatorial optimization problems, the traditional evolutionary algorithms perform worse due to the low efficiency for generating the offspring by a stochastic mechanism. To address the issue, a multi-objective combinatorial generative adversarial optimization method is proposed to make the algorithm capable of learning the implicit information embodied in the evolution process. After classifying the optimal non-dominated solutions in the current generation as real data, the generative adversarial network (GAN) is trained by them, with the purpose of learning their distribution information. The Adam algorithm that employs the adaptively learning rate for each parameter is introduced to update the main parameters of GAN. Following that, an offspring reproduction strategy is designed to form a new feasible solution from the decimal output of the generator. To further verify the rationality of the proposed method, it is applied to solve the participant selection problem of the crowdsensing and the detailed offspring reproduction strategy is given. The experimental results for the crowdsensing systems with various tasks and participants show that the proposed algorithm outperforms the others in both convergence and distribution.

Hour-Ahead Energy Trading Management with Demand Forecasting in Microgrid Considering Power Flow Constraints

Multiple small-scale low-voltage distribution networks with distributed generators can be connected in a radial pattern to form a multi-bus medium voltage microgrid. Additionally, each bus has an independent operator that can manage its power supply and demand. Since the microgrid operates in the market-oriented mode, the bus operators aim to maximize their own benefits and expect to protect their privacy. Accordingly, in this paper, a distributed hour-ahead energy trading management is proposed. First, the benefit optimization problem of the microgrid is solved, which is decomposed into the local benefit optimization sub problems of buses. Then, the local sub problems can be solved by the negotiation of operators with their neighbors. Additionally, the reference demand before negotiation is forecasted by the neural network rather than given in advance. Furthermore, the power flow constraints are considered to guarantee the operational stability. Meanwhile, the power loss minimization is considered in the objective function. Finally, the demonstration and simulation cases are given to validate the effectiveness of the proposed hour-ahead energy trading management.

An Improved Grey Wolf Optimization Algorithm with Variable Weights

With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed. And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed. Simulation experiments are carried out, and comparisons are made. Results show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm (BA). The novel VW-GWO algorithm is also verified in high-dimensional problems.