A Sampling-Based Method for Generating Nondominated Solutions in Stochastic MOMP Problems

2003 ◽  
pp. 119-145
Author(s):  
Samuel B. Graves ◽  
Jeffrey L. Ringuest ◽  
Andrés L. Medaglia
Keyword(s):  
Nondominated Solutions

scholarly journals A Hybrid Strategic Oscillation with Path Relinking Algorithm for the Multiobjective k-Balanced Center Location Problem

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 853
Author(s):  
Jesús Sánchez-Oro ◽  
Ana D. López-Sánchez ◽  
Anna Martínez-Gavara ◽  
Alfredo G. Hernández-Díaz ◽  
Abraham Duarte
Keyword(s):  
Location Problem ◽  
State Of The Art ◽  
Path Relinking ◽  
Multiobjective Evolutionary Algorithms ◽  
Demand Point ◽  
High Quality ◽  
Nondominated Solutions ◽  
Center Location ◽  
Strategic Oscillation ◽  
N Demand

This paper presents a hybridization of Strategic Oscillation with Path Relinking to provide a set of high-quality nondominated solutions for the Multiobjective k-Balanced Center Location problem. The considered location problem seeks to locate k out of m facilities in order to serve n demand points, minimizing the maximum distance between any demand point and its closest facility while balancing the workload among the facilities. An extensive computational experimentation is carried out to compare the performance of our proposal, including the best method found in the state-of-the-art as well as traditional multiobjective evolutionary algorithms.

Optimal Structural Design of a Heat Sink With Laminar Single-Phase Flow Using Computational Fluid Dynamics-Based Multi-Objective Genetic Algorithm

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Ya Ge ◽  
Feng Shan ◽  
Zhichun Liu ◽  
Wei Liu
Keyword(s):  
Genetic Algorithm ◽  
Heat Sink ◽  
Volumetric Flow Rate ◽  
Pareto Solutions ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Conflicting Objectives ◽  
Order Preference ◽  
General Method

This paper proposes a general method combining evolutionary algorithm and decision-making technique to optimize the structure of a minichannel heat sink (MCHS). Two conflicting objectives, the thermal resistance θ and the pumping power P, are simultaneously considered to assess the performance of the MCHS. In order to achieve the ultimate optimal design, multi-objective genetic algorithm is employed to obtain the nondominated solutions (Pareto solutions), while technique for order preference by similarity to an ideal solution (TOPSIS) is employed to determine which is the best compromise solution. Meanwhile, both the material cost and volumetric flow rate are fixed where this nonlinear problem is solved by applying the penalty function. The results show that θ of Pareto solutions varies from 0.03707 K W−1 to 0.10742 K W−1, while P varies from 0.00307 W to 0.05388 W, respectively. After the TOPSIS selection, it is found that P is significantly reduced without increasing too much θ. As a result, θ and P of the optimal MCHS determined by TOPSIS are 35.82% and 52.55% lower than initial one, respectively.

scholarly journals Multiobjective Sensor Ontology Matching Technique with User Preference Metrics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hai Zhu ◽  
Xingsi Xue ◽  
Chengcai Jiang ◽  
He Ren
Keyword(s):  
Similarity Measures ◽  
User Preference ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Nondominated Solutions ◽  
Network Applications ◽  
Data Heterogeneity ◽  
Ontology Alignment Evaluation Initiative ◽  
Selection Operator

Due to the problem of data heterogeneity in the semantic sensor networks, the communications among different sensor network applications are seriously hampered. Although sensor ontology is regarded as the state-of-the-art knowledge model for exchanging sensor information, there also exists the heterogeneity problem between different sensor ontologies. Ontology matching is an effective method to deal with the sensor ontology heterogeneity problem, whose kernel technique is the similarity measure. How to integrate different similarity measures to determine the alignment of high quality for the users with different preferences is a challenging problem. To face this challenge, in our work, a Multiobjective Evolutionary Algorithm (MOEA) is used in determining different nondominated solutions. In particular, the evaluating metric on sensor ontology alignment’s quality is proposed, which takes into consideration user’s preferences and do not need to use the Reference Alignment (RA) beforehand; an optimization model is constructed to define the sensor ontology matching problem formally, and a selection operator is presented, which can make MOEA uniformly improve the solution’s objectives. In the experiment, the benchmark from the Ontology Alignment Evaluation Initiative (OAEI) and the real ontologies of the sensor domain is used to test the performance of our approach, and the experimental results show the validity of our approach.

Constraint Handling in Particle Swarm Optimization

2012 ◽  
pp. 60-80
Author(s):  
Wen Fung Leong ◽  
Gary G. Yen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Constraint Handling ◽  
Hidden Information ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Local Searches ◽  
Multiobjective Approach ◽  
Simulation Results ◽  
Fine Tune

In this article, the authors propose a particle swarm optimization (PSO) for constrained optimization. The proposed PSO adopts a multiobjective approach to constraint handling. Procedures to update the feasible and infeasible personal best are designed to encourage finding feasible regions and convergence toward the Pareto front. In addition, the infeasible nondominated solutions are stored in the global best archive to exploit the hidden information for guiding the particles toward feasible regions. Furthermore, the number of feasible personal best in the personal best memory and the scalar constraint violations of personal best and global best are used to adapt the acceleration constants in the PSO flight equations. The purpose is to find more feasible particles and search for better solutions during the process. The mutation procedure is applied to encourage global and fine-tune local searches. The simulation results indicate that the proposed constrained PSO is highly competitive, achieving promising performance.

Solving Environmental/Economic Dispatch Problem

2010 ◽  
pp. 123-145
Author(s):  
M.A. Abido
Keyword(s):  
Environmental Economic ◽  
Economic Dispatch ◽  
Test System ◽  
Standard Test ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Multiobjective Particle Swarm Optimization

Multiobjective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) problem is proposed and presented in this work. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposed MOPSO technique has been implemented to solve the EED problem with competing and non-commensurable cost and emission objectives. Several optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed MOPSO technique to generate a set of well-distributed Pareto-optimal solutions in one single run. The comparison with the different reported techniques demonstrates the superiority of the proposed MOPSO in terms of the diversity of the Pareto optimal solutions obtained. In addition, a quality measure to Pareto optimal solutions has been implemented where the results confirm the potential of the proposed MOPSO technique to solve the multiobjective EED problem and produce high quality nondominated solutions.

Constraint Handling in Particle Swarm Optimization

2010 ◽  
Vol 1 (1) ◽  
pp. 42-63 ◽  
Author(s):  
Wen Fung Leong ◽  
Gary G. Yen
Keyword(s):  
Particle Swarm Optimization ◽  
Pareto Front ◽  
Particle Swarm ◽  
Constraint Handling ◽  
Hidden Information ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Local Searches ◽  
Multiobjective Approach ◽  
Fine Tune

In this article, the authors propose a particle swarm optimization (PSO) for constrained optimization. The proposed PSO adopts a multiobjective approach to constraint handling. Procedures to update the feasible and infeasible personal best are designed to encourage finding feasible regions and convergence toward the Pareto front. In addition, the infeasible nondominated solutions are stored in the global best archive to exploit the hidden information for guiding the particles toward feasible regions. Furthermore, the number of feasible personal best in the personal best memory and the scalar constraint violations of personal best and global best are used to adapt the acceleration constants in the PSO flight equations. The purpose is to find more feasible particles and search for better solutions during the process. The mutation procedure is applied to encourage global and fine-tune local searches. The simulation results indicate that the proposed constrained PSO is highly competitive, achieving promising performance.

Mean-Variance Model for the Build-Operate-Transfer Scheme Under Demand Uncertainty

2003 ◽  
Vol 1857 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Anthony Chen ◽  
Kitti Subprasom ◽  
Zhaowang Ji
Keyword(s):  
Demand Uncertainty ◽  
Network Design Problem ◽  
Transfer Scheme ◽  
Nondominated Solutions ◽  
Variance Model ◽  
Traffic Demand ◽  
Simulation Based ◽  
Capacity Selection ◽  
Mean Variance

A mean-variance model was developed for determining the optimal toll and capacity in a build-operate-transfer (BOT) roadway project subject to traffic demand uncertainty. This mean-variance model involves two objectives: maximizing mean profit and minimizing the variance (or standard deviation) of profit. The variance associated with profit is considered as a risk. Because maximizing expected profit and minimizing risk are often conflicting, there may not be a single best solution that can simultaneously optimize both objectives. Hence, it is necessary to explicitly consider this as a multiobjective problem so that a set of nondominated solutions can be generated. In this study, the optimal toll and capacity selection for the BOT problem under demand uncertainty is formulated as a special case of the stochastic network design problem. A simulation-based multiobjective genetic algorithm was developed to solve this stochastic bilevel mathematical programming formulation. Numerical results are also presented as a case study.

Multi-Objective Cuckoo Search Under Multiple Archiving Strategies

2016 ◽  
Vol 15 (04) ◽  
pp. 1650020 ◽  
Author(s):  
Kamel Zeltni ◽  
Souham Meshoul
Keyword(s):  
Performance Metrics ◽  
Cuckoo Search ◽  
Nonparametric Test ◽  
Critical Issue ◽  
Test Problems ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Bounded Set ◽  
Recent Addition

Cuckoo Search (CS) is a recent addition to the field of swarm-based metaheuristics. It has been shown to be an efficient approach for global optimization. Moreover, its application for solving Multi-objective Optimization (MOO) shows very promising results as well. In multi-objective context, a bounded archive is required to store the set of nondominated solutions. But, what is the best archiving strategy to use in order to maintain a bounded set with good characteristics is a critical issue that may lead to a questionable choice. In this work, the behavior of the developed multi-objective CS is studied under several archiving strategies. An extensive experimental study has been conducted using several test problems and two performance metrics related to convergence and diversity. A nonparametric test for statistical analysis is performed. In addition, we used a Multi-Objective Particle Swarm Optimization (MOPSO) for further analysis and comparison. The results revealed that archiving strategies play an important role as they can impact differently on the quality of obtained fronts depending on the problem’s characteristics. Also, this study confirms that the proposed MOCS algorithm is a very promising approach for MOPs compared to the widely used MOPSO.

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