Survey of quality measures for multi-objective optimization: Construction of complementary set of multi-objective quality measures

Laszczyk, Maciej; Myszkowski, Paweł B.

doi:10.1016/j.swevo.2019.04.001

Cited by 61 publications

(24 citation statements)

References 32 publications

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“…This set of optimal solutions is known as the Pareto front. On the Pareto front, each solution has a different level of compromise between the design objectives of the proposed MOP (that is, no solution is better than another) [51].…”

Section: Background Of Multi-objective Optimizationmentioning

confidence: 99%

A Loop Pairing Method for Non-Linear Multivariable Control Systems Under a Multi-Objective Optimization Approach

et al. 2020

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In the multivariable control literature there are few techniques that face the problem of selecting suitable loop pairings in non-linear multivariable systems. Most techniques analyze the linearized system at a specific operating point. This paper proposes a new methodology to optimally and simultaneously select the loop pairings and the tuning of the parameters of the decentralized control by applying a multi-objective optimization approach directly on the non-linear system. The main contribution of this work is that the proposed methodology enables a detailed multi-dimensional analysis of the performances and trade-offs in the available loop pairings to control a multivariable non-linear system. The methodology is applied in this paper to three examples that analyze how the different types of loop pairings conflict. In one of the examples, the proposed methodology was applied first in the linearized system and later in the non-linear system. The results were contradictory and show how the application of loop pairing techniques for linear systems can be inaccurate when they are applied on a non-linear system previously linearized at an operating point. The following examples show that the operating point of a non-linear system, the design objectives of each multiobjective problem, as well as the designer's preferences have important roles in the selection of an optimal loop pairing. INDEX TERMS Multivariable control system, non-linear systems, loop pairing, decentralized control structures, multi-objective evolutionary optimization, Pareto front.

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Section: Background Of Multi-objective Optimizationmentioning

confidence: 99%

A Loop Pairing Method for Non-Linear Multivariable Control Systems Under a Multi-Objective Optimization Approach

et al. 2020

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“…A set of QMs presented in [11] is used to verify the effectiveness of the methods. Convergence, diversity of the PF approximation, and the efficiency of the method is verified.…”

Section: B Quality Measuresmentioning

confidence: 99%

“…A set of quality measures (QMs) proposed in [11] is used to evaluate the multi-objective results. Convergence and diversity of the resulting PF approximations are measured, along with the efficiency of the method.…”

Section: Introductionmentioning

confidence: 99%

A Specialized Evolutionary Approach to the bi-objective Travelling Thief Problem

Laszczyk¹,

Myszkowski²

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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In the recent years, it has been shown that real world-problems are often comprised of two, interdependent subproblems. Often, solving them independently does not lead to the solution to the entire problem. In this article, a Travelling Thief Problem is considered, which combines a Travelling Salesman Problem with a Knapsack Problem. A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is investigated, along with its recent modification-a Non-Dominated Tournament Genetic Algorithm (NTGA). Each method is investigated in two configurations. One, with generic representation, and genetic operators. The other, specialized to the given problem, to show how the specialization of genetic operators leads to better results. The impact of the modifications introduced by NTGA is verified. A set of Quality Measures is used to verify the convergence, and diversity of the resulting PF approximations, and efficiency of the method. A set of experiments is carried out. It is shown that both methods work almost the same when generic representation is used. However, NTGA outperforms classical NSGA-II in the specialized results.

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“…To evaluate the results, the QMs proposed in [6] are used. This section contains their description along with the description of reference points required for their calculation.…”

Section: B Quality Measuresmentioning

confidence: 99%

“…The results are evaluated by measuring convergence and diversity of the PF approximation and efficiency of the method. The set of Quality Measures (QMs) proposed in [6] is used. Moreover, visualizations of selected results are provided and a thorough theoretical analysis is presented.…”

Section: Introductionmentioning

confidence: 99%

Non-dominated Sorting Tournament Genetic Algorithm for Multi-Objective Travelling Salesman Problem

Myszkowski

Laszczyk

Dziadek

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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A Travelling Salesman Problem (TSP) is an NPhard combinatorial problem that is very important for many real-world applications. In this paper, it is shown, that proposed approach solves multi-objective TSP (mTSP) more effectively than other investigated methods, i.e. Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed methods use rank and crowding distance (well-known from NSGA-II), combining those mechanisms in a novel, unique way: competing and coevolving in the evolution process. The proposed modifications are investigated and verified by the benchmark mTSP instances, and results are compared to other methods.

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Survey of quality measures for multi-objective optimization: Construction of complementary set of multi-objective quality measures

Cited by 61 publications

References 32 publications

A Loop Pairing Method for Non-Linear Multivariable Control Systems Under a Multi-Objective Optimization Approach

A Loop Pairing Method for Non-Linear Multivariable Control Systems Under a Multi-Objective Optimization Approach

A Specialized Evolutionary Approach to the bi-objective Travelling Thief Problem

Non-dominated Sorting Tournament Genetic Algorithm for Multi-Objective Travelling Salesman Problem

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