Self-adaptive Multi-objective Differential Evolution Algorithm Research Articles

A Self-Adaptive Multiobjective Differential Evolution Algorithm for the Unrelated Parallel Batch Processing Machine Scheduling Problem

In this paper, the unrelated parallel batch processing machine (UPBPM) scheduling problem is addressed to minimize the total energy consumption (TEC) and makespan. Firstly, a mixed-integer line programming model (MILP) of the UPBPM scheduling problem is presented. Secondly, a self-adaptive multiobjective differential evolution (AMODE) algorithm is put forward. Since the parameter value can affect the performance of the algorithm greatly, an adaptive parameter control method is proposed according to the convergence index of the individual and the evolution degree of the population to improve the exploitation and exploration ability of the algorithm. Meanwhile, an adaptive mutation strategy is proposed to improve the algorithm’s convergence and the solutions’ diversity. Finally, to verify the effectiveness of the algorithm, comparative experiments are carried out on 20 instances with 5 different scales. Numerical comparisons indicate that the proposed method can achieve high comprehensive performance.

A Self-Adaptive Multi-Objective Differential Evolution Algorithm Applied to the Styrene Reactor Optimization

A Self-Adaptive Multi-Objective Differential Evolution Algorithm Applied to the Styrene Reactor Optimization

Self-adaptive multi-objective differential evolution algorithm with first front elitism for optimizing network usage in networked control systems

In networked control systems, by means of event-triggered transmission, it is possible to reduce the network usage, keeping the control system performance at satisfactory levels. There are several schemes for event-triggered transmission. In this study, we propose a multi-objective optimization problem to tune the event-triggered mechanisms. On solving the proposed problem by means of multi-objective evolutionary optimization, a set of efficient solutions is generated with different tradeoffs between control system performance and the number of transmissions. To solve the proposed problem, we also developed an improved multi-objective differential evolution algorithm that includes a self-adaptive mechanism, dynamic crowding distance operator, and novel elitism of the first front. The proposed method is applied to tune decentralized event-triggered mechanisms for a controller given a priori, considering random network-induced delays and packet loss. Two case studies are present ed, comparing the performance of eight different decentralized event-triggered schemes, analyzing the selection of the sampling period, and demonstrating the efficacy of the proposed tuning method.

A Modified Multiobjective Self-Adaptive Differential Evolution Algorithm and Its Application on Optimization Design of the Nuclear Power System

A modified multiobjective self-adaptive differential evolution algorithm (MMOSADE) is presented in this paper to improve the accuracy of multiobjective optimization design in the nuclear power system. The performance of the MMOSADE is tested by the ZDT test function set and compared with classical evolutionary algorithms. The results indicate that MMOSADE has a better performance in convergence and diversity. Based on the MMOSADE, a multiobjective optimization design platform for the nuclear power system is proposed, and the application of which is carried out. The evaluation program of the PRHR-HX in AP1000 is developed, and its reliability is verified. The optimal design schemes of PHHR-HX are obtained by utilizing the multiobjective optimization design platform. The results show that the optimal design schemes can envelop the prototype design scheme. This conclusion proves that the optimization design platform proposed in this paper is effective and feasible.

A multi-stage stochastic programming approach for blood supply chain planning

Abstract Perishability of blood products and uncertainty in donation and demand sizes complicate the blood supply chain planning. This paper presents a novel bi-objective mixed-integer model for integrated collection, production/screening, distribution and routing planning of blood products, and seeks to simultaneously optimize the total cost and freshness of transported blood products to hospitals. To cope with inherent uncertainty of input data, a multi-stage stochastic programming approach with a combined scenario tree is presented. Due to the high complexity of the problem, a novel hybrid multi-objective self-adaptive differential evolution algorithm is developed, which benefits from the variable neighborhood search with fuzzy dominance sorting (hereafter it is briefly called MSDV). MSDV is validated through comparing its performance with two of the most common multi-objective evolutionary algorithms (i.e. MOICA and NSGA-II). Applicability of the proposed decision model is also tested through a real case study. Our results show that the solution efficiency of a network can be balanced with its effectiveness through customer satisfaction. Further, several sensitivity analyses are carried out to provide valuable managerial insights.

Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm

Abstract Microgrid systems, such as solar photovoltaic (PV) power and wind energy, integrated with diesel generators are promising energy supplies and are economically feasible for current and future use in relation to increased demands for energy and depletion of conventional sources. It is thus important to optimize the size of hybrid microgrid system (HMS) components, including storage, to determine system cost and reliability. In this paper, optimal sizing of a PV/wind/diesel HMS with battery storage is conducted using the Multi-Objective Self-Adaptive Differential Evolution (MOSaDE) algorithm for the city of Yanbu, Saudi Arabia. Using the multi-objective optimization approach, the objectives are treated simultaneously and independently, thereby leading to a reduction in computational time. One of the main criteria to consider when designing and optimizing the HMS is the energy management strategy, which is required to coordinate the different units comprising the HMS. The multi-objective optimization approach is then used to analyze the Loss of Power Supply Probability (LPSP), the Cost of Electricity (COE), and the Renewable Factor (RF) in relation to HMS cost and reliability and is tested using three case studies involving differing house numbers. Results verify its application in optimizing the HMS and in its practical implementation. In addition, optimization results using the proposed approach provided a set of design solutions for the HMS, which will assist researchers and practitioners in selecting the optimal HMS configuration. Moreover, it is important to select optimally sized HMS components to ensure that all load demands are met at the minimum energy cost and high reliability.

Multi-objective optimization of p -xylene oxidation process using an improved self-adaptive differential evolution algorithm

Abstract The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p -xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1] . PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simultaneously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization problems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

Multi-Objective Aerodynamic Optimization Design and Data Mining of a High Pressure Ratio Centrifugal Impeller

An automated three-dimensional multi-objective optimization and data mining method is presented by integrating a self-adaptive multi-objective differential evolution algorithm (SMODE), 3D parameterization method for blade profile and meridional channel, Reynolds-averaged Navier–Stokes (RANS) solver technique and data mining technique of self-organizing map (SOM). Using this method, redesign of a high pressure ratio centrifugal impeller is conducted. After optimization, 16 optimal Pareto solutions are obtained. Detailed aerodynamic analysis indicates that the aerodynamic performance of the optimal Pareto solutions is greatly improved. By SOM-based data mining on optimized solutions, the interactions among objective functions and significant design variables are analyzed. The mechanism behind parameter interactions is also analyzed by comparing the data mining results with the performance of typical designs.

Design of a Parallel Manipulator Based on Multi-Objective Self-Adaptive Differential Evolution Algorithm

In order to improve highly non-isotropic input-output relations in the optimal design of a parallel robot, this paper presents a method based on a multi-objective self-adaptive differential evolution (MOSaDE) algorithm.The approach considers a solution-diversity mechanism coupled with a memory of those sub-optimal solutions found during the process. In theMOSaDE algorithm, both trial vector generation strategies and their associated control parameter values were gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings could be determined adaptively to match different phases of the search processevolution.Furthermore, a constraint-handling mechanism is added to bias the search to the feasible region of the search space. The obtained solution will be a set of optimal geometric parameters and optimal PID control gains. The empirical analysis of thenumerical results shows the efficiency of the proposed algorithm.

Optimization of p-xylene oxidation reaction process based on self-adaptive multi-objective differential evolution

Purified terephthalic acid (PTA) is used for producing a variety of polyesters. In the production of PTA, p-xylene (PX) is first transformed into terephthalic acid (TA) by oxidation process and then TA is refined. As a key step, the oxidation of PX to TA is a significant chemical process of PTA production. To improve qualified product yield with low energy consumption, in this paper, multi-objective optimization of various conflicting objectives (namely minimization of combustion loss, maximization of TA yield) is conducted using self-adaptive multi-objective differential evolution algorithm (SADE). The main characteristic of it is that DE's trial vector generation strategies and the corresponding control parameters are gradually self-adjusted adaptively based on the knowledge learnt from the previous searches in generating improved solutions. Furthermore, to handle constraints in multi-objective problems, the pseudo feasible concept is proposed to effectively utilize the critical information carried by some infeasible solutions. Optimization results of PX oxidation reaction process indicate that application of SADE can greatly improve the yield of TA with low combustion loss without degenerating TA quality. Furthermore, SADE can provide a set of Pareto optimal solutions and then suitable multi-criterion decision-making techniques can be employed to select one or a small set of the optimal solution(s) of design parameter(s) based on preference.