Improved performance in multi-objective optimization using external archive

Sadhana ◽  
2020 ◽  
Vol 45 (1) ◽  
Author(s):  
Mahesh B Patil
Keyword(s):  
Multi Objective Optimization ◽  
Multi Objective ◽  
External Archive ◽  
Improved Performance

scholarly journals Adjusting normalization bounds to improve hypervolume based search for expensive multi-objective optimization

2021 ◽  
Author(s):  
Bing Wang ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Numerical Experiments ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Promising Candidate ◽  
Multi Objective ◽  
Improved Performance

AbstractWhen solving expensive multi-objective optimization problems, surrogate models are often used to reduce the number of true evaluations. Based on predictions from the surrogate models, promising candidate solutions, also referred to as infill solutions, can be identified for evaluation to expedite the search towards the optimum. This infill process in turn involves optimization of certain criteria derived from the surrogate models. In this study, predicted hypervolume maximization is considered as the infill criterion for expensive multi/many-objective optimization. In particular, we examine the effect of normalization bounds on the performance of the algorithm building on our previous study on bi-objective optimization. We propose a more scalable approach based on “surrogate corner” search that shows improved performance where some of the conventional techniques face challenges. Numerical experiments on a range of benchmark problems with up to 5 objectives demonstrate the efficacy and reliability of the proposed approach.

A Multi-Objective Gravitational Search Algorithm Based on Non-Dominated Sorting

2012 ◽  
Vol 3 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Hadi Nobahari ◽  
Mahdi Nikusokhan ◽  
Patrick Siarry
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravitational Acceleration ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Mutation Operators ◽  
External Archive ◽  
Gravitational Search ◽  
New Criterion

This paper proposes an extension of the Gravitational Search Algorithm (GSA) to multi-objective optimization problems. The new algorithm, called Non-dominated Sorting GSA (NSGSA), utilizes the non-dominated sorting concept to update the gravitational acceleration of the particles. An external archive is also used to store the Pareto optimal solutions and to provide some elitism. It also guides the search toward the non-crowding and the extreme regions of the Pareto front. A new criterion is proposed to update the external archive and two new mutation operators are also proposed to promote the diversity within the swarm. Numerical results show that NSGSA can obtain comparable and even better performances as compared to the previous multi-objective variant of GSA and some other multi-objective optimization algorithms.

Multi-Objective Fish School Search

2015 ◽  
Vol 6 (1) ◽  
pp. 23-40 ◽  
Author(s):  
Carmelo J. A. Bastos-Filho ◽  
Augusto C. S. Guimarães
Keyword(s):  
Multi Objective Optimization ◽  
New Approach ◽  
Fish School ◽  
Multi Objective ◽  
External Archive ◽  
Conflicting Objectives ◽  
Adaptive Schemes ◽  
Convergence Problems ◽  
Pareto Fronts ◽  
Future Work

The authors propose in this paper a very first version of the Fish School Search (FSS) algorithm for Multi-Objective Optimization. The proposal allows the optimization of problems with two or more conflicting objectives. The authors incorporated the dominance concept within the traditional FSS operators, creating a new approach called Multi-objective Fish School Search, MOFSS. They also adapted the barycenter concept deployed in the original FSS, which was replaced by the set of existing solutions in an external archive created to store the non-dominated solutions found during the search process. From their results in the DTLZ set of benchmark functions, the authors observed that the MOFSS obtained a similar performance when compared to well-known and well-established multi-objective swarm-based optimization algorithms. They detected some convergence problems in functions with a high number of local Pareto fronts. However, adaptive schemes can be used in future work to overcome this weakness.

A hybrid method of multi-objective particle swarm optimization and k-means clustering and its application to modal parameter estimation in the time–frequency domain

2019 ◽  
Vol 26 (9-10) ◽  
pp. 769-778
Author(s):  
Kai Yang ◽  
Kaiping Yu ◽  
Hui Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Method ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Modal Parameters ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Time Frequency ◽  
Multi Objective ◽  
External Archive

Modal parameters provide an insight into the dynamical properties of structures. In the time–frequency domain–based methods, time–frequency ridges contain crucial information on the characteristics of multicomponent signals, and manually extracting time–frequency ridges is a huge burden, especially when long-time time-varying modal parameters are focused on. In this study, time–frequency ridge extraction is converted into a multi-objective optimization problem, and a new hybrid method of multi-objective particle swarm optimization and k-means clustering is proposed to solve such a multi-objective optimization problem. In the hybrid method, the particle swarm is partitioned into sub-swarms by k-means clustering, and the sub-swarms are used to search new solutions for updating a finite-sized external archive, which is used as the exclusive centroids of the k-means clustering. Simultaneously, the finite-sized external archive serves as global best positions of sub-swarms. Both simulated and experimental cases are applied to validate the hybrid method. With the aid of the hybrid method, the influence of varying temperatures on modal parameters of a column beam is experimentally analyzed in detail.

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