Adaptive Multi-objective Differential Evolution Algorithm Research Articles

Retracted: Medical Multimedia Image Analysis of Scoliosis in Children Aged 3–7 Years Based on Adaptive Multiobjective Differential Evolution Algorithm

Retracted: Medical Multimedia Image Analysis of Scoliosis in Children Aged 3–7 Years Based on Adaptive Multiobjective Differential Evolution Algorithm

WITHDRAWN: Short term optimal scheduling of a virtual power plants integrated with electrothermal hybrid energy storage system using an effective adaptive multi-objective differential evolution algorithm

virtual power plants (VPPs) in the power distribution grid play an important role in balancing the power and heat generation and consumption in residential, commercial, and industrial sectors. This study proposes VPPs for risk-averse energy exchange of resources and customers in the presence of the hybrid storage system. An appropriate energy management program is presented to make a balance between generators and consumers in VPPs considering generation uncertainties including electricity demand and price forecasting in the electricity market and renewable energy uncertainties. Hence, monetary exchange methods such as day-ahead agreements are proposed for energy exchange among VPPs. The expanded exchange structure of the suggested framework simplifies the renewable energy's sources to the electricity market despite its small-scale and non-dispatchable features. Also, a novel model is presented for short-term programing of virtual power plant considering technical and economic uncertainties. Given that VPPs can enter the electricity market based on time-of-use (TOU) pricing strategy and participation in demand response (DR) program, minimizing the expected cost of energy generation and environmental pollution is considered as the objective function. A multi-objective efficient model of energy management employing the machine speed scaling mechanism is suggested. An effective adaptive multi-objective differential evolution (AMODE) algorithm is presented to solve the multi-objective optimization problem. AMODE usages a novel Fitness Evaluation Mechanism (FEM) using dynamic reference point and fuzzy association randomness study to evaluate solutions in the evolutionary population by implementing the conditional value at risk (CVaR) method for risk management. The simulation studies and sensitivity analysis results are evaluated to confirm the effectiveness of the proposed model on a VPP in the island and grid-connected modes with and without participation in DR program. Finally, the conceptual results are discussed.

Optimization of energy-efficient open shop scheduling with an adaptive multi-objective differential evolution algorithm

There have been growing interests in the energy-efficient production scheduling recently because of the growing shortage of energy. Open shop scheduling problem (OSSP) is a kind of common but seldom concerned production scheduling problem. This paper focuses on energy-efficient OSSP (EOSSP), where the effect of setup operation on production efficiency and energy consumption is considered. A multi-objective energy-efficient model based on machine speed scaling mechanism is proposed. To handle this multi-objective problem, we propose an effective adaptive multi-objective differential evolution (AMODE) algorithm. The AMODE uses a new fitness evaluation mechanism (FEM) based on dynamic reference point and fuzzy correlation entropy analysis to assess the solutions in evolution population. It also uses an adaptive opposition-based learning (AOBL) to improve its local search ability. Taguchi method is utilized to obtain the best combination of critical parameters of the AMODE. The proposed mathematical model is validated with CPLEX, and a lexicographic method is used to determine the preferable solution. Experimental results show that both our proposed FEM and AOBL can improve the performance of AMODE. Extensive experiments reveal that the performance of AMODE is superior to the other three well-known algorithms in addressing the EOSSP.

Medical Multimedia Image Analysis of Scoliosis in Children Aged 3–7 Years Based on Adaptive Multiobjective Differential Evolution Algorithm

Scoliosis is mainly caused by the spinal column deviating from its centerline during the growth process. It usually occurs in children aged 3–7 years old. After a detailed analysis of the existing scoliosis detection methods, this paper compares and analyzes the advantages of using multimedia image processing technology to check scoliosis in children aged 3–7 years and uses an adaptive multiobjective differential evolution algorithm to perform scoliosis comprehensive testing. Finally, the experimental analysis shows that the adaptive multiobjective differential evolution algorithm is applied to scoliosis detection to analyze the characteristics of children's cervical spine curvature and the relationship between the cervical spine curvature and the scoliosis angle.

Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm

Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm

Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm

In this paper, for the operations of wastewater treatment processes (WWTPs), an intelligent multi-objective optimization control (IMOOC), based on an adaptive multi-objective differential evolution (AMODE) algorithm, is proposed to search for the suitable set-points to balance the treatment performance and the operational costs. In this IMOOC, the combination of an AMODE algorithm and the multi-objective critical issues helps us to fulfill all the control objectives simultaneously. To improve the optimization efficiency and achieve fast convergence, the AMODE algorithm is designed to improve the local search and the global exploration abilities: The adaptive adjustment strategies are developed to select the suitable scaling factor and crossover rate in the process of searching. Meanwhile, the multi-objective critical issues, according to the state of the processes, are given as a nonlinear multi-objective optimization problem to evaluate the operational performance of WWTPs. Therefore, once the nonlinear multi-objective optimization problem is solved at each sampling time, the most appropriate set of Pareto is selected as suitable set-points to achieve the process performance. To demonstrate the merits of our proposed method, the proposed IMOOC is applied to the Benchmark Simulation Model No. 1 of WWTPs. The results show that the proposed IMOOC effectively provides process control. The performance comparison with other algorithms also indicates that the proposed optimal strategy yields better effluent qualities and lower average operation consumption.

Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation

Abstract This study investigates an adaptive fuzzy neural network control system for the multiobjective operation of wastewater treatment process (WWTP) with standard effluent quality (EQ) as well as low energy consumption (EC). The control system consists of an optimization module with the adaptive multiobjective differential evolution (AMODE) algorithm and a control module with the adaptive fuzzy neural network (AFNN). First, an AMODE algorithm, using the adaptive adjustment strategies for selecting the suitable scaling factor and crossover rate, is developed to optimize all objectives simultaneously. Then, the optimal set-points of the dissolved oxygen concentration in the fifth tank ( S O5 ) and the nitrogen nitrate concentration in the second anoxic tank ( S NO2 ) of WWTP can be obtained by the AMODE algorithm. Second, an AFNN controller, based on an adaptive second order algorithm, is employed to trace the set-points of S O5 and S NO2 for achieving the process performance. Finally, the proposed control system is applied on the Benchmark Simulation Model 1 (BSM1). The performance comparison with other algorithms indicates that the proposed control system yields better effluent qualities and lower average operation consumption.

Dual problem of sorptive barrier design with a multiobjective approach

Sorptive barrier technology is a recently developed tool to separate hazardous contaminants from friendly environment. The design of sorptive barrier refers to configuring different amendments with sorptive ability of organic pollutant, which is an integer programming problem and a relatively time consuming problem as well. In this paper, sorptive barrier design is newly modeled in a biobjective optimization approach, in which the dual problem of sorptive barrier design is deduced. The objectives are to minimize the financial cost and the amount of pollutant leaking through barriers. Then an opposition-based adaptive multiobjective differential evolution algorithm (MODEA-OA) is applied to handle the proposed model. The Pareto optimal front obtained by MODEA-OA spreads accurately and evenly in all three instances tested. To select extreme optimal solutions, the original and dual sorptive barrier design problems can be solved simultaneously. This study suggests that modeling barrier design as a multiobjective optimization problem is an effective approach.

Operating Point Optimization of Auxiliary Power Unit Using Adaptive Multi-Objective Differential Evolution Algorithm

Series hybrid electric vehicles improvements in fuel consumption and emissions directly depend on the operating point of the auxiliary power unit (APU). In order to balance the conflicting goals of fuel consumption and emissions reduction in the choice of operating point, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem with competing objectives of fuel-electricity conversion cost, HC emissions, CO emissions, and NOx emissions first. Then, the adaptive multi-objective differential evolution (AMODE) algorithm is proposed to solve the APU operating point multi-objective optimization problem. It adopts an external elitist archive to store the nondominated solutions that are found during the evolutionary process. The innovative adaptive mutation operator of the AMODE ensures its high searching ability, and the adaptive grids mechanism improves the diversity of the resulted Pareto solutions effectively. Finally, bench experiments under four typical driving cycles are performed and comparisons are made between the results of the proposed multi-objective optimization approach (MOA), the exponential weighted approach, and the traditional single objective approach. The experimental results show that the proposed AMODE-based MOA improves the APU emissions significantly, at the expense of a slight drop in fuel efficiency.

A grid-based adaptive multi-objective differential evolution algorithm

Differential evolution is an excellent optimizer for single objective optimization problems. To extend its use for multi-objective optimization problems with promising performance, this paper proposes a grid-based adaptive multi-objective differential evolution algorithm. The main feature of the proposed algorithm is its dynamical adjustment of convergence and diversity by exploiting the feedback information during the evolutionary process. In the algorithm, the objective space is divided into grids according to the nondominated solutions in the population. Based on the grid, three indexes including grid fitness, grid density, and grid-objective-wise standard deviation are defined to measure individual rank, individual density, and population search status quo, respectively. Afterwards, three main components of the algorithm, i.e., parents selection, parameter control, and population update, are implemented based on grid index values. To validate algorithm performance, comprehensive experiments are carried out on thirty-one benchmark problems. The results show that the proposed algorithm outperforms nine state-of-the-art competitors in terms of three performance metrics. Also, the effectiveness of three components and the sensitivity of two design parameters in the algorithm are empirically quantified.