scholarly journals Prioritized optimization by Nash games : towards an adaptive multi-objective strategy

2021 ◽  
Vol 71 ◽  
pp. 54-63
Author(s):  
Jean-Antoine Désidéri ◽  
Régis Duvigneau
Keyword(s):  
Optimal Solution ◽  
Optimization Method ◽  
Cost Functions ◽  
Second Phase ◽  
Two Phase ◽  
Nash Games ◽  
Multi Objective ◽  
Business Jet ◽  
Decreasing Functions

This work is part of the development of a two-phase multi-objective differentiable optimization method. The first phase is classical: it corresponds to the optimization of a set of primary cost functions, subject to nonlinear equality constraints, and it yields at least one known Pareto-optimal solution xA*. This study focuses on the second phase, which is introduced to permit to reduce another set of cost functions, considered as secondary, by the determination of a continuum of Nash equilibria, {x̅ε} (ε≥ 0), in a way such that: firstly, x̅0=xA* (compatibility), and secondly, for ε sufficiently small, the Pareto-optimality condition of the primary cost functions remains O(ε2), whereas the secondary cost functions are linearly decreasing functions of ε. The theoretical results are recalled and the method is applied numerically to a Super-Sonic Business Jet (SSBJ) sizing problem to optimize the flight performance.

scholarly journals A novel two-phase cycle algorithm for effective cyber intrusion detection in edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yiguang Gong ◽  
Yunping Liu ◽  
Chuanyang Yin
Keyword(s):  
Intrusion Detection ◽  
Optimal Parameter ◽  
Edge Computing ◽  
Cloud Services ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Multi Objective ◽  
Phase Cycle ◽  
Positive Rate

AbstractEdge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.

scholarly journals Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing

2019 ◽  
Vol 11 (9) ◽  
pp. 2619 ◽  
Author(s):  
Wei He ◽  
Guozhu Jia ◽  
Hengshan Zong ◽  
Jili Kong
Keyword(s):  
Service Management ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Multiple Objectives ◽  
Second Phase ◽  
Time Cost ◽  
Two Phase ◽  
Preference Information ◽  
Multi Objective ◽  
Service Selection And Scheduling

Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg.

Multi-Objective Trajectory Optimization of Free-Floating Space Manipulator Using NSGA-II

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu
Keyword(s):  
Trajectory Optimization ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Joint Space ◽  
Motion Path ◽  
Objective Functions ◽  
Nsga Ii ◽  
Space Manipulator ◽  
Multi Objective

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.

scholarly journals Multi-objective performance optimization of ORC cycle based on improved ant colony algorithm

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 48-59 ◽  
Author(s):  
Rong He ◽  
Xinli Wei ◽  
Nasruddin Hassan
Keyword(s):  
Performance Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Second Law Of Thermodynamics ◽  
Optimization Method ◽  
Ant Colony ◽  
Multi Objective ◽  
Objective Performance ◽  
Performance Evaluation Index ◽  
Improved Ant Colony Algorithm

Abstract To solve the problem of multi-objective performance optimization based on ant colony algorithm, a multi-objective performance optimization method of ORC cycle based on an improved ant colony algorithm is proposed. Through the analysis of the ORC cycle system, the thermodynamic model of the ORC system is constructed. Based on the first law of thermodynamics and the second law of thermodynamics, the ORC system evaluation model is established in a MATLAB environment. The sensitivity analysis of the system is carried out by using the system performance evaluation index, and the optimal working parameter combination is obtained. The ant colony algorithm is used to optimize the performance of the ORC system and obtain the optimal solution. Experimental results show that the proposed multi-objective performance optimization method based on the ant colony algorithm for the ORC cycle needs a shorter optimization time and has a higher optimization efficiency.

scholarly journals Decision space robustness for multi-objective integer linear programming

2021 ◽  
Author(s):  
Michael Stiglmayr ◽  
José Rui Figueira ◽  
Kathrin Klamroth ◽  
Luís Paquete ◽  
Britta Schulze
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Efficient Solutions ◽  
Second Phase ◽  
Decision Space ◽  
Two Phase ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Linear Programming Problems ◽  
Consistency Properties

AbstractIn this article we introduce robustness measures in the context of multi-objective integer linear programming problems. The proposed measures are in line with the concept of decision robustness, which considers the uncertainty with respect to the implementation of a specific solution. An efficient solution is considered to be decision robust if many solutions in its neighborhood are efficient as well. This rather new area of research differs from robustness concepts dealing with imperfect knowledge of data parameters. Our approach implies a two-phase procedure, where in the first phase the set of all efficient solutions is computed, and in the second phase the neighborhood of each one of the solutions is determined. The indicators we propose are based on the knowledge of these neighborhoods. We discuss consistency properties for the indicators, present some numerical evaluations for specific problem classes and show potential fields of application.

scholarly journals Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation

2011 ◽  
Vol 22 (4) ◽  
pp. 379-389
Author(s):  
Ricardo C. Silva ◽  
Edilson F. Arruda ◽  
Fabrício O. Ourique
Keyword(s):  
Probability Distributions ◽  
Phase Method ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Term Operation ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Programming

This work presents a novel framework to address the long term operation of a class of multi-objective programming problems. The proposed approach considers a stochastic operation and evaluates the long term average operating costs/profits. To illustrate the approach, a two-phase method is proposed which solves a prescribed number of K mono-objective problems to identify a set of K points in the Pareto-optimal region. In the second phase, one searches for a set of non-dominated probability distributions that define the probability that the system operates at each point selected in the first phase, at any given operation period. Each probability distribution generates a vector of average long-term objectives and one solves for the Pareto-optimal set with respect to the average objectives. The proposed approach can generate virtual operating points with average objectives that need not have a feasible solution with an equal vector of objectives. A few numerical examples are presented to illustrate the proposed method.

Development of an Optimization Method for Determination of Process Sequences Considering Prismatic Workpieces

1994 ◽  
Author(s):  
Joachim G. Taiber
Keyword(s):  
Process Planning ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Manufacturing Cost ◽  
Tool Paths ◽  
Set Up ◽  
Definition Of ◽  
Optimization Of Cutting Parameters

Abstract In the field of process planning most of the working time is spent for the following activities: machine tool and tool assignment determination of the set-up and process sequence definition of tool paths optimization of cutting parameters The quality how these activities are done influence substantially manufacturing cost that are to be minimized. The more complex a planning task is the more difficult it is for the planner to work out an optimal solution within reasonable time. In this article it will be discussed how process planning of prismatic workpieces considering drilling and milling machining can be optimized by computer assistance.

A Tradeoff-Based Interactive Multi-Objective Optimization Method Driven by Evolutionary Algorithms

2017 ◽  
Vol 21 (2) ◽  
pp. 284-292 ◽  
Author(s):  
Lu Chen ◽  
◽  
Bin Xin ◽  
Jie Chen ◽  
◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Optimal Solution ◽  
Optimization Method ◽  
Normal Vector ◽  
Pareto Optimal ◽  
Management Problem ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Tangent Hyperplane ◽  
Conflicting Objectives

Multi-objective optimization problems involve two or more conflicting objectives, and they have a set of Pareto optimal solutions instead of a single optimal solution. In order to support the decision maker (DM) to find his/her most preferred solution, we propose an interactive multi-objective optimization method based on the DM’s preferences in the form of indifference tradeoffs. The method combines evolutionary algorithms with the gradient-based interactive step tradeoff (GRIST) method. An evolutionary algorithm is used to generate an approximate Pareto optimal solution at each iteration. The DM is asked to provide indifference tradeoffs whose projection onto the tangent hyperplane of the Pareto front provides a tradeoff direction. An approach for approximating the normal vector of the tangent hyperplane is proposed which is used to calculate the projection. A water quality management problem is used to demonstrate the interaction process of the interactive method. In addition, three benchmark problems are used to test the accuracy of the normal vector approximation approach and compare the proposed method with GRIST.

scholarly journals Optimum design of 3R robot manipulador by using Improved Differential Evolution implemented in parallel computation

2020 ◽  
Vol 1 (2) ◽  
pp. 83-103
Author(s):  
Sezimária De Fátima Pereira Saramago ◽  
José Laércio Doricio ◽  
Milena Almeida Leite Brandão
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Robot Manipulator ◽  
Optimal Solution ◽  
Optimization Method ◽  
Objective Functions ◽  
Basic Algorithm ◽  
Promising Alternative ◽  
Multi Objective ◽  
De Algorithm

In recent decades the great interest in Evolutionary Algorithms (EAs) has boosted their development leading to a significant improvement in their efficiency and applicability. Thus, EAs have been applied to solve optimization problems in different areas of knowledge. A promising optimization method known as Differential Evolution (DE), which belongs to the class of AEs, has attracted the attention of researchers. The DE algorithm is simple, robust and efficient. However, by testing with classical optimization problems noticed that sometimes the results obtained with DE are not as satisfactory as expected or that in many cases the algorithm ends the search for the optimal solution prematurely. Recently, with the advancement and greater availability of computer technology, the scientific community has been thinking about the implementation of optimization algorithms in parallel in order to reduce the processing time. The main objective of this paper is to present an improvement of the Differential Evolution optimization method, proposing modifications to the basic algorithm by using shuffled complex and making it able to work with parallel computing. The proposed methodology is applied to the optimal design of an orthogonal 3R robot manipulator that takes into account the characteristics of its workspace. For this purpose, a multi-objective optimization problem is formulated to obtain the optimal geometric parameters for the robot. The maximum workspace volume, the maximum system stiffness and the optimum dexterity are considered as the multi-objective functions. The results show that the procedure represents a promising alternative for the type of problem presented above.

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