Solving Multi-Objective Problems by Мeans of Single Objective Solver

Abstract With the development of economy, more and more people travel by plane. Many airports have added satellite halls to relieve the pressure of insufficient boarding gates in airport terminals. However, the addition of satellite halls will have a certain impact on connecting flights of transit passengers and increase the difficulty of reasonable allocation of flight and gate in airports. Based on the requirements and data of question F of the 2018 postgraduate mathematical contest in modeling, this paper studies the flight-gate allocation of additional satellite halls at airports. Firstly, match the seven types of flights with the ten types of gates. Secondly, considering the number of gates used and the least number of flights not allocated to the gate, and adding the two factors of the overall tension of passengers and the minimum number of passengers who failed to transfer, the multi-objective 0–1 programming model was established. Determine the weight vector $w=(0.112,0.097,0.496,0.395)$ w = ( 0.112 , 0.097 , 0.496 , 0.395 ) of objective function by entropy value method based on personal preference, then the multi-objective 0–1 programming model is transformed into single-objective 0–1 programming model. Finally, a graph coloring algorithm based on parameter adjustment is used to solve the transformed model. The concept of time slice was used to determine the set of time conflicts of flight slots, and the vertex sequences were colored by applying the principle of “first come first serve”. Applying the model and algorithm proposed in this paper, it can be obtained that the average value of the overall tension degree of passengers minimized in question F is 35.179%, the number of flights successfully allocated to the gate maximized is 262, and the number of gates used is minimized to be 60. The corresponding flight-gate difficulty allocation weight is $\alpha =0.32$ α = 0.32 and $\beta =0.40$ β = 0.40 , and the proportion of flights successfully assigned to the gate is 86.469%. The number of passengers who failed to transfer was 642, with a failure rate of 23.337%.

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Chaotic Evolution Algorithm with Elite Strategy in Single-objective and Multi-objective Optimization

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc42975.2020.9283144 ◽

2020 ◽

Author(s):

Yan Pei

Keyword(s):

Multi Objective Optimization ◽

Multi Objective ◽

Elite Strategy ◽

Evolution Algorithm ◽

Single Objective

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A two-sided matching decision-making approach based on regret theory under intuitionistic fuzzy environment

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202720 ◽

2021 ◽

pp. 1-18

Author(s):

Xiang Jia ◽

Xinfan Wang ◽

Yuanfang Zhu ◽

Lang Zhou ◽

Huan Zhou

Keyword(s):

Decision Making ◽

Hamming Distance ◽

Optimal Solution ◽

Intuitionistic Fuzzy Sets ◽

Regret Theory ◽

Multi Objective ◽

Intuitionistic Fuzzy ◽

Fuzzy Environment ◽

Objective Model ◽

Single Objective

This study proposes a two-sided matching decision-making (TSMDM) approach by combining the regret theory under the intuitionistic fuzzy environment. At first, according to the Hamming distance of intuitionistic fuzzy sets and regret theory, superior and inferior flows are defined to describe the comparative preference of subjects. Hereafter, the satisfaction degrees are obtained by integrating the superior and inferior flows of the subjects. The comprehensive satisfaction degrees are calculated by aggregating the satisfaction degrees, based on which, a multi-objective TSMDM model is built. Furthermore, the multi-objective TSMDM model is converted to a single-objective model, the optimal solution of the latter is derived. Finally, an illustrative example and several analyses are provided to verify the feasibility and the effectiveness of the proposed approach.

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Multi-objective immune genetic algorithm solving dynamic single-objective multimodal constrained optimization

2012 8th International Conference on Natural Computation ◽

10.1109/icnc.2012.6234765 ◽

2012 ◽

Cited By ~ 1

Author(s):

Zhuhong Zhang ◽

Min Liao ◽

Lei Wang

Keyword(s):

Genetic Algorithm ◽

Constrained Optimization ◽

Immune Genetic Algorithm ◽

Multi Objective ◽

Single Objective

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The Position of ADME Predictions in Multi-Objective QSAR

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.2021010101 ◽

2021 ◽

Vol 6 (1) ◽

pp. 1-8

Author(s):

Anna Tsantili-Kakoulidou

Keyword(s):

Drug Efficacy ◽

Clinical Development ◽

Efficacy And Safety ◽

Huge Amount ◽

Multi Objective ◽

Drug Candidates ◽

Drug Molecules ◽

Qsar Models ◽

Adme Properties ◽

Single Objective

ADME properties and toxicity predictions play an essential role in prioritization and optimization of drug molecules. According to recent statistics, drug efficacy and safety are principal reasons for drug failure. In this perspective, the position of ADME predictions in the evolution of traditional QSAR from the single objective of biological activity to a multi-task concept is discussed. The essential features of ADME and toxicity QSAR models are highlighted. Since such models are applied to prioritize existing or virtual project compounds with already established or predicted target affinity, a mechanistic interpretation, although desirable, is not a primary goal. However, a broad applicability domain is crucial. A future challenge with multi-objective QSAR is to adapt to the realm of big data by integrating techniques for the exploitation of the continuously increasing number of ADME data and the huge amount of clinical development endpoints for the sake of efficacy and safety of new drug candidates.

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Enhancement of Labyrinth Seals Efficiency by Means of a Multi-Objective Optimization Technique

Volume 5B: Heat Transfer ◽

10.1115/gt2018-75782 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mikhail Gritckevich ◽

Kunyuan Zhou ◽

Vincent Peltier ◽

Markus Raben ◽

Olga Galchenko

Keyword(s):

Optimization Technique ◽

Leakage Flow ◽

Multi Objective Optimization ◽

Labyrinth Seals ◽

Engine Operation ◽

Turbine Engine ◽

Multi Objective ◽

Single Objective ◽

Ansys Workbench ◽

Comprehensive Study

A comprehensive study of several labyrinth seals has been performed in the framework of both single-objective and multi-objective optimizations with the main focus on the effect of stator grooves formed due to the rubbing during gas turbine engine operation. For that purpose, the developed optimization workflow based on the DLR-AutoOpti optimizer and ANSYS-Workbench CAE environment has been employed to reduce the leakage flow and windage heating for several seals. The obtained results indicate that the seal designs obtained from optimizations without stator grooves have worse performance during the lifecycle than those with the stator grooves, justifying the importance of considering this effect for real engineering applications.

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EVOLUTIONARY MULTI-OBJECTIVE OPTIMISATION OF NEURAL NETWORKS FOR FACE DETECTION

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026804001288 ◽

2004 ◽

Vol 04 (03) ◽

pp. 237-253 ◽

Cited By ~ 25

Author(s):

STEFAN WIEGAND ◽

CHRISTIAN IGEL ◽

UWE HANDMANN

Keyword(s):

Search Strategy ◽

Human Face ◽

Video Streams ◽

Feed Forward Neural Network ◽

Image Region ◽

Multi Objective ◽

Strategy Adaptation ◽

Gradient Based ◽

Speed And Accuracy ◽

Single Objective

For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a feed-forward neural network (NN), e.g. in the Viisage-FaceFINDER® video surveillance system. We describe the optimisation of such a NN by a hybrid algorithm combining evolutionary multi-objective optimisation (EMO) and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy. We compare an EMO and a single objective approach, both with online search strategy adaptation. It turns out that EMO is preferable to the single objective approach in several respects.

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Comparing Multi-Objective Local Search Algorithms for the Beam Angle Selection Problem

Mathematics ◽

10.3390/math10010159 ◽

2022 ◽

Vol 10 (1) ◽

pp. 159

Author(s):

Guillermo Cabrera-Guerrero ◽

Carolina Lagos

Keyword(s):

Local Search ◽

Organs At Risk ◽

Intensity Modulated Radiation Therapy ◽

Selection Problem ◽

Large Set ◽

Beam Angle ◽

Local Search Algorithms ◽

Multi Objective ◽

Single Objective ◽

Therapy Treatment

In intensity-modulated radiation therapy, treatment planners aim to irradiate the tumour according to a medical prescription while sparing surrounding organs at risk as much as possible. Although this problem is inherently a multi-objective optimisation (MO) problem, most of the models in the literature are single-objective ones. For this reason, a large number of single-objective algorithms have been proposed in the literature to solve such single-objective models rather than multi-objective ones. Further, a difficulty that one has to face when solving the MO version of the problem is that the algorithms take too long before converging to a set of (approximately) non-dominated points. In this paper, we propose and compare three different strategies, namely random PLS (rPLS), judgement-function-guided PLS (jPLS) and neighbour-first PLS (nPLS), to accelerate a previously proposed Pareto local search (PLS) algorithm to solve the beam angle selection problem in IMRT. A distinctive feature of these strategies when compared to the PLS algorithms in the literature is that they do not evaluate their entire neighbourhood before performing the dominance analysis. The rPLS algorithm randomly chooses the next non-dominated solution in the archive and it is used as a baseline for the other implemented algorithms. The jPLS algorithm first chooses the non-dominated solution in the archive that has the best objective function value. Finally, the nPLS algorithm first chooses the solutions that are within the neighbourhood of the current solution. All these strategies prevent us from evaluating a large set of BACs, without any major impairment in the obtained solutions’ quality. We apply our algorithms to a prostate case and compare the obtained results to those obtained by the PLS from the literature. The results show that algorithms proposed in this paper reach a similar performance than PLS and require fewer function evaluations.

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