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

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.

Download Full-text

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

Sensors ◽

10.3390/s21082775 ◽

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.

Download Full-text

Multi-objective Optimization of a Steering Linkage Using Alternative Objective Functions

Lecture Notes in Computer Science - Advances in Swarm Intelligence ◽

10.1007/978-3-030-26354-6_5 ◽

2019 ◽

pp. 47-58 ◽

Cited By ~ 2

Author(s):

Suwin Sleesongsom ◽

Sujin Bureerat

Keyword(s):

Objective Functions ◽

Multi Objective Optimization ◽

Multi Objective

Download Full-text

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

ASME/BATH 2017 Symposium on Fluid Power and Motion Control ◽

10.1115/fpmc2017-4235 ◽

2017 ◽

Cited By ~ 1

Author(s):

Andrew J. Robison ◽

Andrea Vacca

Keyword(s):

Genetic Algorithm ◽

Design Method ◽

Third Party ◽

Low Flow ◽

Objective Functions ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Generation Algorithm ◽

Multi Objective ◽

Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

Download Full-text

Design Improvement by Sensitivity Analysis (DISA) Under Interval Uncertainty Using Multi-Objective Optimization

Volume 5: 35th Design Automation Conference, Parts A and B ◽

10.1115/detc2009-87127 ◽

2009 ◽

Cited By ~ 1

Author(s):

J. Hamel ◽

M. Li ◽

S. Azarm

Keyword(s):

Sensitivity Analysis ◽

Interval Uncertainty ◽

Minimal Amount ◽

Engineering System ◽

Objective Functions ◽

Multi Objective Optimization ◽

New Approach ◽

Design Improvement ◽

Multi Objective ◽

System A

Uncertainty in the input parameters to an engineering system may not only degrade the system’s performance, but may also cause failure or infeasibility. This paper presents a new sensitivity analysis based approach called Design Improvement by Sensitivity Analysis (DISA). DISA analyzes the interval parameter uncertainty of a system and, using multi-objective optimization, determines an optimal combination of design improvements required to enhance performance and ensure feasibility. This is accomplished by providing a designer with options for both uncertainty reduction and, more importantly, slight design adjustments. The approach can provide improvements to a design of interest that will ensure a minimal amount of variation in the objective functions of the system while also ensuring the engineering feasibility of the system. A two stage sequential framework is used in order to effectively employ metamodeling techniques to approximate the analysis function of an engineering system and greatly increase the computational efficiency of the approach. This new approach has been applied to two engineering examples of varying difficulty to demonstrate its applicability and effectiveness.

Download Full-text

Multi-Objective Optimization of Metal Forming Processes Based on the Kalai and Smorodinsky Solution

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.651-653.1387 ◽

2015 ◽

Vol 651-653 ◽

pp. 1387-1393 ◽

Cited By ~ 2

Author(s):

Lorenzo Iorio ◽

Lionel Fourment ◽

Stephane Marie ◽

Matteo Strano

Keyword(s):

Optimization Problems ◽

Good Method ◽

Wire Drawing ◽

Bargaining Problem ◽

Mathematical Function ◽

Objective Functions ◽

Multi Objective Optimization ◽

Multi Objective ◽

Cooperative Approach ◽

The Game Theory

The Game Theory is a good method for finding a compromise between two players in a bargaining problem. The Kalai and Smorodinsky (K-S) method is a solution the bargaining problem where players make decisions in order to maximize their own utility, with a cooperative approach. Interesting applications of the K-S method can be found in engineering multi-objective optimization problems, where two or more functions must be minimized. The aim of this paper is to develop an optimization algorithm aimed at rapidly finding the Kalai and Smorodinsky solution, where the objective functions are considered as players in a bargaining problem, avoiding the search for the Pareto front. The approach uses geometrical consideration in the space of the objective functions, starting from the knowledge of the so-called Utopia and Nadir points. An analytical solution is proposed and initially tested with a simple minimization problem based on a known mathematical function. Then, the algorithm is tested (thanks to a user friendly routine built-in the finite element code Forge®) for FEM optimization problem of a wire drawing operation, with the objective of minimizing the pulling force and the material damage. The results of the simulations are compared to previous works done with others methodologies.

Download Full-text

Multi-Objective Design Optimization of the Leg Mechanism for a Piping Inspection Robot

Volume 5A: 38th Mechanisms and Robotics Conference ◽

10.1115/detc2014-34057 ◽

2014 ◽

Cited By ~ 3

Author(s):

Renaud Henry ◽

Damien Chablat ◽

Mathieu Porez ◽

Frédéric Boyer ◽

Daniel Kanaan

Keyword(s):

Nuclear Power ◽

Pareto Front ◽

Design Parameters ◽

Objective Functions ◽

Multi Objective Optimization ◽

Dimensional Synthesis ◽

Force Factor ◽

Multi Objective ◽

Inspection Robot ◽

Contact Points

This paper addresses the dimensional synthesis of an adaptive mechanism of contact points ie a leg mechanism of a piping inspection robot operating in an irradiated area as a nuclear power plant. This studied mechanism is the leading part of the robot sub-system responsible of the locomotion. Firstly, three architectures are chosen from the literature and their properties are described. Then, a method using a multi-objective optimization is proposed to determine the best architecture and the optimal geometric parameters of a leg taking into account environmental and design constraints. In this context, the objective functions are the minimization of the mechanism size and the maximization of the transmission force factor. Representations of the Pareto front versus the objective functions and the design parameters are given. Finally, the CAD model of several solutions located on the Pareto front are presented and discussed.

Download Full-text

A Memetic Optimization Strategy Based on Dimension Reduction in Decision Space

Evolutionary Computation ◽

10.1162/evco_a_00122 ◽

2015 ◽

Vol 23 (1) ◽

pp. 69-100 ◽

Cited By ~ 13

Author(s):

Handing Wang ◽

Licheng Jiao ◽

Ronghua Shang ◽

Shan He ◽

Fang Liu

Keyword(s):

Dimension Reduction ◽

Optimization Problems ◽

State Of The Art ◽

Search Space ◽

Optimization Strategy ◽

Objective Functions ◽

Multi Objective Optimization ◽

Decision Space ◽

Multi Objective ◽

Decision Variables

There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced, which causes some redundancy during the search in a decision space. In response to this scenario, we propose a novel memetic (multi-objective) optimization strategy based on dimension reduction in decision space (DRMOS). DRMOS firstly analyzes the mapping relation between decision variables and objective functions. Then, it reduces the dimension of the search space by dividing the decision space into several subspaces according to the obtained relation. Finally, it improves the population by the memetic local search strategies in these decision subspaces separately. Further, DRMOS has good portability to other multi-objective evolutionary algorithms (MOEAs); that is, it is easily compatible with existing MOEAs. In order to evaluate its performance, we embed DRMOS in several state of the art MOEAs to facilitate our experiments. The results show that DRMOS has the advantage in terms of convergence speed, diversity maintenance, and portability when solving MOPs with an unbalanced mapping relation between decision variables and objective functions.

Download Full-text