scholarly journals CORAL-DMOEA: Correlation Alignment-Based Information Transfer for Dynamic Multi-Objective Optimization (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13765-13766
Author(s):  
Li Chen ◽  
Hua Xu
Keyword(s):  
Information Transfer ◽  
Optimization Problems ◽  
Optimization Process ◽  
Transfer Model ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Essential Characteristic ◽  
Multi Objective ◽  
Highly Correlated

One essential characteristic of dynamic multi-objective optimization problems is that Pareto-Optimal Front/Set (POF/POS) varies over time. Tracking the time-dependent POF/POS is a challenging problem. Since continuous environments are usually highly correlated, past information is critical for the next optimization process. In this paper, we integrate CORAL methodology into a dynamic multi-objective evolutionary algorithm, named CORAL-DMOEA. This approach employs CORAL to construct a transfer model which transfer past well-performed solutions to form an initial population for the next optimization process. Experimental results demonstrate that CORAL-DMOEA can effectively improve the quality of solutions and accelerate the evolution process.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

scholarly journals Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm

2020 ◽  
Author(s):  
Tomohiro Harada ◽  
Misaki Kaidan ◽  
Ruck Thawonmas
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Computing Time ◽  
The Other ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Evaluation Time ◽  
Surrogate Function

Abstract This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D.

scholarly journals A Decomposition-Based Interactive Method for Multi-Objective Evolutionary Algorithm

2012 ◽  
Author(s):  
Nguye Long ◽  
Bui Thu Lam
Keyword(s):  
Optimization Problems ◽  
Decision Makers ◽  
Optimization Process ◽  
Multi Objective Optimization ◽  
Interactive Method ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Single Solution ◽  
Optimal Set

Multi-objectivity has existed in many real-world optimization problems. In most multi-objective cases, objectives are often conflicting, there is no single solution being optimal with regards to all objectives. These problems are called Multi-objective Optimization Problems (MOPs). To date, there have been al large number of methods for solving MOPs including evolutionary methods (namly Multi-objective Evolutionary Algorithms MOEAs). With the use of a population of solutions for searching. MOEAs are naturally suitable for approximating optimal solutions (called the Pareto Optimal Set (POS) or the efficient set). There has been a popular trend in MOEAs considering the role of Decision Makers (DMs) during the optimization process (known as the human-in-loop) for checking, analyzing the results and giving the preference to guide the optimization process. This is call the interactive method.

Mixture Surrogate Models for Multi- Objective Optimization

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Shailesh S. Kadre ◽  
Vipin K. Tripathi
Keyword(s):  
Finite Element ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Optimization Techniques ◽  
Optimization Process ◽  
Multiple Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Costly Function ◽  
Selection Of

Multi-objective optimization problems (MOOP) involve minimization of more than one objective functions and all of them are to be simultaneously minimized. The solution of these problems involves a large number of iterations. The multi- objective optimization problems related structural optimization of complex engineering structures is usually solved with finite element analysis (FEA). The solution time required to solve these FEA based solutions are very high. So surrogate models or meta- models are used to approximate the finite element solution during the optimization process. These surrogate assisted multi- objective optimization techniques are very commonly used in the current literature. These optimization techniques use evolutionary algorithm and it is very difficult to guarantee the convergence of the final solution, especially in the cases where the budget of costly function evaluations is low. In such cases, it is required to increase the efficiency of surrogate models in terms of accuracy and total efforts required to find the final solutions.In this paper, an advanced surrogate assisted multi- objective optimization algorithm (ASMO) is developed. This algorithm can handle linear, equality and non- linear constraints and can be applied to both benchmark and engineering application problems. This algorithm does not require any prior knowledge for the selection of surrogate models. During the optimization process, best single and mixture surrogate models are automatically selected. The advanced surrogate models are created by MATSuMoTo, the MATLAB based tool box. These mixture models are built by Dempster- Shafer theory (DST). This theory has a capacity to handle multiple model characteristics for the selection of best models. By adopting this strategy, it is ensured that most accurate surrogate models are selected. There can be different kind of surrogate models for objective and constraint functions. Multi-objective optimization of machine tool spindle is studied as the test problem for this algorithm and it is observed that the proposed strategy is able to find the non- dominated solutions with minimum number of costly function evaluations. The developed method can be applied to other benchmark and engineering applications.

scholarly journals Effect of the Profile of the Decision Maker in the Search for Solutions in the Decision-Making Process

2021 ◽  
Vol 26 (2) ◽  
pp. 28
Author(s):  
Mercedes Perez-Villafuerte ◽  
Laura Cruz-Reyes ◽  
Nelson Rangel-Valdez ◽  
Claudia Gomez-Santillan ◽  
Héctor Fraire-Huacuja
Keyword(s):  
Optimization Problems ◽  
Region Of Interest ◽  
Multi Objective Optimization ◽  
Ordinal Classification ◽  
Multi Objective ◽  
Portfolio Problem ◽  
Real Size ◽  
Early Phases ◽  
Incorporation Method

Many real-world optimization problems involving several conflicting objective functions frequently appear in current scenarios and it is expected they will remain present in the future. However, approaches combining multi-objective optimization with the incorporation of the decision maker’s (DM’s) preferences through multi-criteria ordinal classification are still scarce. In addition, preferences are rarely associated with a DM’s characteristics; the preference selection is arbitrary. This paper proposes a new hybrid multi-objective optimization algorithm called P-HMCSGA (preference hybrid multi-criteria sorting genetic algorithm) that allows the DM’s preferences to be incorporated in the optimization process’ early phases and updated into the search process. P-HMCSGA incorporates preferences using a multi-criteria ordinal classification to distinguish solutions as good and bad; its parameters are determined with a preference disaggregation method. The main feature of P-HMCSGA is the new method proposed to associate preferences with the characterization profile of a DM and its integration with ordinal classification. This increases the selective pressure towards the desired region of interest more in agreement with the DM’s preferences specified in realistic profiles. The method is illustrated by solving real-size multi-objective PPPs (project portfolio problem). The experimentation aims to answer three questions: (i) To what extent does allowing the DM to express their preferences through a characterization profile impact the quality of the solution obtained in the optimization? (ii) How sensible is the proposal to different profiles? (iii) How much does the level of robustness of a profile impact the quality of final solutions (this question is related with the knowledge level that a DM has about his/her preferences)? Concluding, the proposal fulfills several desirable characteristics of a preferences incorporation method concerning these questions.

scholarly journals Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization

2021 ◽  
Author(s):  
Haijuan Zhang ◽  
Gai-Ge Wang
Keyword(s):  
Transfer Learning ◽  
Pareto Front ◽  
Optimization Problems ◽  
Prediction Method ◽  
Reference Points ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Centroid Distance ◽  
Dynamic Strategy

AbstractMulti-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions obey different distributions in decision space and the performance of NSGA-III when dealing with DMOPs should be further improved. In this paper, centroid distance is proposed and combined into NSGA-III with transfer learning together for DMOPs, called TC_NSGAIII. Centroid distance-based strategy is regarded as a prediction method to prevent some inappropriate individuals through measuring the distance of the population centroid and reference points. After the distance strategy, transfer learning is used for generating an initial population using the past experience. To verify the effectiveness of our proposed algorithm, NSGAIII, Tr_NSGAIII (NSGA-III combining with transfer learning only), Ce_NSGAIII (NSGA-III combining with centroid distance only), and TC_NSGAIII are compared. Seven state-of-the-art algorithms have been used for comparison on CEC 2015 benchmarks. Besides, transfer learning and centroid distance are regarded as a dynamic strategy, which is incorporated into three static algorithms, and the performance improvement is measured. What’s more, twelve benchmark functions from CEC 2015 and eight sets of parameters in each function are used in our experiments. The experimental results show that the performance of algorithms can be greatly improved through the proposed approach.

scholarly journals An Improved Multi-Objective Evolutionary Approach for Aerospace Shell Production Scheduling Problem

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 509
Author(s):  
Qing Wang ◽  
Xiaoshuang Wang ◽  
Haiwei Luo ◽  
Jian Xiong
Keyword(s):  
Evolutionary Algorithm ◽  
Production Scheduling ◽  
Production Line ◽  
Optimization Problems ◽  
Structural Features ◽  
Optimization Process ◽  
Scheduling Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Shell Production

To certain degree, multi-objective optimization problems obey the law of symmetry, for instance, the minimum of one objective function corresponds to the maximum of another objective. To provide effective support for the multi-objective operation of the aerospace product shell production line, this paper studies multi-objective aerospace shell production scheduling problems. Firstly, a multi-objective optimization model for the production scheduling of aerospace product shell production lines is established. In the presented model, the maximum completion time and the cost of production line construction are optimized simultaneously. Secondly, to tackle the characteristics of discreteness, non-convexity and strong NP difficulty of the multi-objective problem, a knowledge-driven multi-objective evolutionary algorithm is designed to solve the problem. In the proposed approach, structural features of the scheduling plan are extracted during the optimization process and used to guide the subsequent optimization process. Finally, a set of test instances is generated to illustrate the addressed problem and test the proposed approach. The experimental results show that the knowledge-driven multi-objective evolutionary algorithm designed in this paper has better performance than the two classic multi-objective optimization methods.

MULTI-OBJECTIVE OPTIMIZATION IN RELIABILITY SYSTEM USING GENETIC ALGORITHM AND NEURAL NETWORK

2008 ◽  
Vol 25 (05) ◽  
pp. 649-672 ◽  
Author(s):  
LIANG-HSUAN CHEN ◽  
CHENG-HSIUNG CHIANG
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
System Optimization ◽  
Evolutionary Strategy ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Reliability Systems

To optimize the design of reliability systems, an analyst is frequently faced with the demand of achieving several targets (i.e., maximization of system reliability, minimizations of cost, volume, and weight), some of which may be in conflict with each other. This paper presents a novel hybrid approach, combining a multi-objective genetic algorithm and a neural network, for multi-objective optimization of a reliability system, namely GANNRS (Genetic Algorithm and Neural Network for Reliability System optimization). The multi-objective genetic algorithm's evolutionary strategy is based on the modified neighborhood design, and is presented to find the Pareto optimal solutions so as to provide a variety of compromise solutions to the decision makers. The purpose of the neural network is to generate a good initial population in order to speed up the searching by genetic algorithm. For demonstrating the feasibility of the proposed approach, four multi-objective optimization problems of reliability system are used, and the outcomes are compared with those from other methods. The evidence shows that the proposed GANNRS is more efficient in computation, and the results from the objectives are appealing.

BAT and Hybrid BAT Meta-Heuristic for Quality of Service-Based Web Service Selection

2017 ◽  
Vol 26 (1) ◽  
pp. 123-137 ◽  
Author(s):  
Prashanth Podili ◽  
K.K. Pattanaik ◽  
Prashanth Singh Rana
Keyword(s):  
Service Composition ◽  
Optimization Problems ◽  
Service Selection ◽  
Multi Objective Optimization ◽  
Data Set ◽  
Multi Objective ◽  
Heuristic Techniques ◽  
Web Service Selection ◽  
Discrete Pso

AbstractEfficient QoS-based service selection from a pool of functionally substitutable web services (WS) for constructing composite WS is important for an efficient business process. Service composition based on diverse QoS requirements is a multi-objective optimization problem. Meta-heuristic techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and variants of PSO have been extensively used for solving multi-objective optimization problems. The efficiency of any such meta-heuristic techniques lies with their rate of convergence and execution time. This article evaluates the efficiency of BAT and Hybrid BAT algorithms against the existing GA and Discrete PSO techniques in the context of service selection problems. The proposed algorithms are tested on the QWS data set to select the best fit services in terms of maximum aggregated end-to-end QoS parameters. Hybrid BAT is found to be efficient for service composition.

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