On the arithmetic of infinity oriented implementation of the multi-objective P-algorithm

2012 ◽  
Author(s):  
Antanas Žilinskas
Keyword(s):  
Global Optimization ◽  
Black Box ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Simple Application ◽  
Axiomatic Theory ◽  
Global Optimization Algorithm ◽  
Multi Objective ◽  
Computing Paradigm ◽  
Single Objective

The single-objective P-algorithm is a global optimization algorithm based on a statistical mod- el of objective functions and the axiomatic theory of rational decisions. It has been proven quite suitable for optimization of black-box expensive functions. Recently the P-algorithm has been generalized to multi-objective optimization. In the present paper, the implementation of that algorithm is considered using the new computing paradigm of the arithmetic of infinity. A strong homogeneity of the multi-objective P-algorithm is proven, thus enabling rather a simple application of the algorithm to the problems involving infinities and infinitesimals.

Pareto Artificial Life Algorithm for Multi-Objective Optimization

2011 ◽  
Vol 4 (2) ◽  
pp. 43-60
Author(s):  
Jin-Dae Song ◽  
Bo-Suk Yang
Keyword(s):  
Objective Function ◽  
Artificial Life ◽  
Pareto Set ◽  
Function Optimization ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Function ◽  
Artificial Life Algorithm ◽  
Single Objective

Most engineering optimization uses multiple objective functions rather than single objective function. To realize an artificial life algorithm based multi-objective optimization, this paper proposes a Pareto artificial life algorithm that is capable of searching Pareto set for multi-objective function solutions. The Pareto set of optimum solutions is found by applying two objective functions for the optimum design of the defined journal bearing. By comparing with the optimum solutions of a single objective function, it is confirmed that the single function optimization result is one of the specific cases of Pareto set of optimum solutions.

Pareto Artificial Life Algorithm for Multi-Objective Optimization

2013 ◽  
pp. 100-115
Author(s):  
Jin-Dae Song ◽  
Bo-Suk Yang
Keyword(s):  
Objective Function ◽  
Artificial Life ◽  
Pareto Set ◽  
Function Optimization ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Function ◽  
Artificial Life Algorithm ◽  
Single Objective

Most engineering optimization uses multiple objective functions rather than single objective function. To realize an artificial life algorithm based multi-objective optimization, this paper proposes a Pareto artificial life algorithm that is capable of searching Pareto set for multi-objective function solutions. The Pareto set of optimum solutions is found by applying two objective functions for the optimum design of the defined journal bearing. By comparing with the optimum solutions of a single objective function, it is confirmed that the single function optimization result is one of the specific cases of Pareto set of optimum solutions.

scholarly journals A Decision-Making Tool for a Complex Post Enrollment-Based Course Timetabling

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Ngo Tung Son
Keyword(s):  
Combinatorial Problems ◽  
Mixed Integer ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Spring Semester ◽  
Multi Objective ◽  
Training Costs ◽  
Proposed Model ◽  
Decision Making Tool ◽  
Single Objective

The article describes a new method to construct an enrollment-based course timetable in universities, based on a multi-objective optimization model. The model used mixed-integer and binary variables towards creating a schedule. It satisfies students' preferences for study time, with the number of students in the same class being optimal for training costs while ensuring timetabling business constraints. We use a combination of compromise programming and linear scalarizing to transform many objective functions into single-objective optimization. A scheme of the Genetic Algorithm was developed to solve the proposed model. The proposed method allows approaching several types of multi-objective combinatorial problems. The algorithm was tested by scheduling a study schedule for 3,000 students in the spring semester of 2020 at FPT University, Hanoi, Vietnam. The obtained results show the average students' preference level of 69%. More than 30% of students have a satisfaction level of more than 80% of the timetable after two hours of execution time.

scholarly journals A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

2021 ◽  
Vol 11 (10) ◽  
pp. 4575
Author(s):  
Eduardo Fernández ◽  
Nelson Rangel-Valdez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan
Keyword(s):  
Portfolio Optimization ◽  
Imperfect Information ◽  
Model Parameters ◽  
Final Decision ◽  
Project Portfolio ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Group Member

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

Multiobjective Optimization for High Dimensional Expensively Constrained Black-Box Problems

2021 ◽  
pp. 1-59
Author(s):  
George Cheng ◽  
G. Gary Wang ◽  
Yeong-Maw Hwang
Keyword(s):  
Multiobjective Optimization ◽  
Trust Region ◽  
Adaptive Strategy ◽  
Black Box ◽  
Superior Performance ◽  
High Dimensional ◽  
Multi Objective Optimization ◽  
Semiconductor Substrate ◽  
Multi Objective ◽  
Computationally Expensive

Abstract Multi-objective optimization (MOO) problems with computationally expensive constraints are commonly seen in real-world engineering design. However, metamodel based design optimization (MBDO) approaches for MOO are often not suitable for high-dimensional problems and often do not support expensive constraints. In this work, the Situational Adaptive Kreisselmeier and Steinhauser (SAKS) method was combined with a new multi-objective trust region optimizer (MTRO) strategy to form the SAKS-MTRO method for MOO problems with expensive black-box constraint functions. The SAKS method is an approach that hybridizes the modeling and aggregation of expensive constraints and adds an adaptive strategy to control the level of hybridization. The MTRO strategy uses a combination of objective decomposition and K-means clustering to handle MOO problems. SAKS-MTRO was benchmarked against four popular multi-objective optimizers and demonstrated superior performance on average. SAKS-MTRO was also applied to optimize the design of a semiconductor substrate and the design of an industrial recessed impeller.

Analyzing the Objective Functions for Multi-Objective Optimization of the Amplifier Adaptive Control of Operating Point

2021 ◽  
Author(s):  
Erick A. Barboza ◽  
Carmelo J. A. Bastos-Filho ◽  
Daniel A. R. Chaves ◽  
Joaquim F. Martins-Filho ◽  
Leonardo D. Coelho ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Operating Point ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective
Sign in / Sign up

Export Citation Format

Share Document