scholarly journals A comparative study of many-objective optimizers on large-scale many-objective software clustering problems

2021 ◽  
Author(s):  
Amarjeet Prajapati
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Software Clustering ◽  
Multi Objective ◽  
The Past ◽  
Decision Variables ◽  
Clustering Problems ◽  
Special Case

AbstractOver the past 2 decades, several multi-objective optimizers (MOOs) have been proposed to address the different aspects of multi-objective optimization problems (MOPs). Unfortunately, it has been observed that many of MOOs experiences performance degradation when applied over MOPs having a large number of decision variables and objective functions. Specially, the performance of MOOs rapidly decreases when the number of decision variables and objective functions increases by more than a hundred and three, respectively. To address the challenges caused by such special case of MOPs, some large-scale multi-objective optimization optimizers (L-MuOOs) and large-scale many-objective optimization optimizers (L-MaOOs) have been developed in the literature. Even after vast development in the direction of L-MuOOs and L-MaOOs, the supremacy of these optimizers has not been tested on real-world optimization problems containing a large number of decision variables and objectives such as large-scale many-objective software clustering problems (L-MaSCPs). In this study, the performance of nine L-MuOOs and L-MaOOs (i.e., S3-CMA-ES, LMOSCO, LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA) is evaluated and compared over five L-MaSCPs in terms of IGD, Hypervolume, and MQ metrics. The experimentation results show that the S3-CMA-ES and LMOSCO perform better compared to the LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA in most of the cases. The LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, and DREA, are the average performer, and H-RVEA is the worst performer.

A Memetic Optimization Strategy Based on Dimension Reduction in Decision Space

2015 ◽  
Vol 23 (1) ◽  
pp. 69-100 ◽  
Author(s):  
Handing Wang ◽  
Licheng Jiao ◽  
Ronghua Shang ◽  
Shan He ◽  
Fang Liu
Keyword(s):  
Dimension Reduction ◽  
Optimization Problems ◽  
State Of The Art ◽  
Search Space ◽  
Optimization Strategy ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Multi Objective ◽  
Decision Variables

There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced, which causes some redundancy during the search in a decision space. In response to this scenario, we propose a novel memetic (multi-objective) optimization strategy based on dimension reduction in decision space (DRMOS). DRMOS firstly analyzes the mapping relation between decision variables and objective functions. Then, it reduces the dimension of the search space by dividing the decision space into several subspaces according to the obtained relation. Finally, it improves the population by the memetic local search strategies in these decision subspaces separately. Further, DRMOS has good portability to other multi-objective evolutionary algorithms (MOEAs); that is, it is easily compatible with existing MOEAs. In order to evaluate its performance, we embed DRMOS in several state of the art MOEAs to facilitate our experiments. The results show that DRMOS has the advantage in terms of convergence speed, diversity maintenance, and portability when solving MOPs with an unbalanced mapping relation between decision variables and objective functions.

Evolutionary Large-Scale Multi-Objective Optimization: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-34
Author(s):  
Ye Tian ◽  
Langchun Si ◽  
Xingyi Zhang ◽  
Ran Cheng ◽  
Cheng He ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Future Research ◽  
Decision Variable ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Space Reduction ◽  
Decision Variables ◽  
Comprehensive Survey

Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.

scholarly journals Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

2021 ◽  
Author(s):  
Wen-Jing Hong ◽  
Peng Yang ◽  
Ke Tang
Keyword(s):  
Evolutionary Computation ◽  
Real World ◽  
Large Scale ◽  
Optimization Problems ◽  
Divide And Conquer ◽  
Research Progress ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Decision Variables

AbstractLarge-scale multi-objective optimization problems (MOPs) that involve a large number of decision variables, have emerged from many real-world applications. While evolutionary algorithms (EAs) have been widely acknowledged as a mainstream method for MOPs, most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables. More recently, it has been reported that traditional multi-objective EAs (MOEAs) suffer severe deterioration with the increase of decision variables. As a result, and motivated by the emergence of real-world large-scale MOPs, investigation of MOEAs in this aspect has attracted much more attention in the past decade. This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles. From the key difficulties of the large-scale MOPs, the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables. From the perspective of methodology, the large-scale MOEAs are categorized into three classes and introduced respectively: divide and conquer based, dimensionality reduction based and enhanced search-based approaches. Several future research directions are also discussed.

scholarly journals An Expensive Multi-Objective Optimization Algorithm Based on Decision Space Compression

2021 ◽  
pp. 2159039
Author(s):  
Haosen Liu ◽  
Fangqing Gu ◽  
Yiu-Ming Cheung
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Surrogate Models ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Multi Objective ◽  
The Past ◽  
Decision Variables ◽  
Space Compression

Numerous surrogate-assisted expensive multi-objective optimization algorithms were proposed to deal with expensive multi-objective optimization problems in the past few years. The accuracy of the surrogate models degrades as the number of decision variables increases. In this paper, we propose a surrogate-assisted expensive multi-objective optimization algorithm based on decision space compression. Several surrogate models are built in the lower dimensional compressed space. The promising points are generated and selected in the lower compressed decision space and decoded to the original decision space for evaluation. Experimental studies show that the proposed algorithm achieves a good performance in handling expensive multi-objective optimization problems with high-dimensional decision space.

scholarly journals Improved SparseEA for sparse large-scale multi-objective optimization problems

2021 ◽  
Author(s):  
Yajie Zhang ◽  
Ye Tian ◽  
Xingyi Zhang
Keyword(s):  
Real World ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Limited Budget ◽  
Decision Variables ◽  
Real World Applications

AbstractSparse large-scale multi-objective optimization problems (LSMOPs) widely exist in real-world applications, which have the properties of involving a large number of decision variables and sparse Pareto optimal solutions, i.e., most decision variables of these solutions are zero. In recent years, sparse LSMOPs have attracted increasing attentions in the evolutionary computation community. However, all the recently tailored algorithms for sparse LSMOPs put the sparsity detection and maintenance in the first place, where the nonzero variables can hardly be optimized sufficiently within a limited budget of function evaluations. To address this issue, this paper proposes to enhance the connection between real variables and binary variables within the two-layer encoding scheme with the assistance of variable grouping techniques. In this way, more efforts can be devoted to the real part of nonzero variables, achieving the balance between sparsity maintenance and variable optimization. According to the experimental results on eight benchmark problems and three real-world applications, the proposed algorithm is superior over existing state-of-the-art evolutionary algorithms for sparse LSMOPs.

Multi-Objective Optimization of Metal Forming Processes Based on the Kalai and Smorodinsky Solution

2015 ◽  
Vol 651-653 ◽  
pp. 1387-1393 ◽  
Author(s):  
Lorenzo Iorio ◽  
Lionel Fourment ◽  
Stephane Marie ◽  
Matteo Strano
Keyword(s):  
Optimization Problems ◽  
Good Method ◽  
Wire Drawing ◽  
Bargaining Problem ◽  
Mathematical Function ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cooperative Approach ◽  
The Game Theory

The Game Theory is a good method for finding a compromise between two players in a bargaining problem. The Kalai and Smorodinsky (K-S) method is a solution the bargaining problem where players make decisions in order to maximize their own utility, with a cooperative approach. Interesting applications of the K-S method can be found in engineering multi-objective optimization problems, where two or more functions must be minimized. The aim of this paper is to develop an optimization algorithm aimed at rapidly finding the Kalai and Smorodinsky solution, where the objective functions are considered as players in a bargaining problem, avoiding the search for the Pareto front. The approach uses geometrical consideration in the space of the objective functions, starting from the knowledge of the so-called Utopia and Nadir points. An analytical solution is proposed and initially tested with a simple minimization problem based on a known mathematical function. Then, the algorithm is tested (thanks to a user friendly routine built-in the finite element code Forge®) for FEM optimization problem of a wire drawing operation, with the objective of minimizing the pulling force and the material damage. The results of the simulations are compared to previous works done with others methodologies.

Robustness Against Large Variations in Multi-Objective Optimization Problems

2013 ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis
Keyword(s):  
Optimization Problems ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Design Environment ◽  
Multi Objective ◽  
Uncertainty Sources ◽  
Design Variables ◽  
Robustness Index ◽  
Environment Parameters

In the presence of multiple optimal solutions in multi-modal optimization problems and in multi-objective optimization problems, the designer may be interested in the robustness of those solutions to make a decision. Here, the robustness is related to the sensitivity of the performance functions to uncertainties. The uncertainty sources include the uncertainties in the design variables, in the design environment parameters, in the model of objective functions and in the designer’s preference. There exist many robustness indices in the literature that deal with small variations in the design variables and design environment parameters, but few robustness indices consider large variations. In this paper, a new robustness index is introduced to deal with large variations in the design environment parameters. The proposed index is bounded between zero and one, and measures the probability of a solution to be optimal with respect to the values of the design environment parameters. The larger the robustness index, the more robust the solution with regard to large variations in the design environment parameters. Finally, two illustrative examples are given to highlight the contributions of this paper.

Design of Optimal Operational Parameters for Steam-Alternating-Solvent Processes in Heterogeneous Reservoirs - A Multi-Objective Optimization Approach

2021 ◽  
Author(s):  
Israel Mayo-Molina ◽  
Juliana Y. Leung
Keyword(s):  
Sensitivity Analysis ◽  
Computational Time ◽  
Optimization Approach ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Operational Parameters ◽  
Multi Objective ◽  
Bottom Hole ◽  
Decision Variables ◽  
Conflicting Objectives

Abstract The Steam Alternating Solvent (SAS) process has been proposed and studied in recent years as a new auspicious alternative to the conventional thermal (steam-based) bitumen recovery process. The SAS process incorporates steam and solvent (e.g. propane) cycles injected alternatively using the same configuration as the Steam-Assisted Gravity-Drainage (SAGD) process. The SAS process offers many advantages, including lower capital and operational cost, as well as a reduction in water usage and lower Greenhouse Gas (GHG) Emissions. On the other hand, one of the main challenges of this relatively new process is the influence of uncertain reservoir heterogeneity distribution, such as shale barriers, on production behaviour. Many complex physical mechanisms, including heat transfer, fluid flows, and mass transfer, must be coupled. A proper design and selection of the operational parameters must consider several conflicting objectives. This work aims to develop a hybrid multi-objective optimization (MOO) framework for determining a set of Pareto-optimal SAS operational parameters under a variety of heterogeneity scenarios. First, a 2-D homogeneous reservoir model is constructed based on typical Cold lake reservoir properties in Alberta, Canada. The homogeneous model is used to establish a base scenario. Second, different shale barrier configurations with varying proportions, lengths, and locations are incorporated. Third, a detailed sensitivity analysis is performed to determine the most impactful parameters or decision variables. Based on the results of the sensitivity analysis, several objective functions are formulated (e.g., minimizing energy and solvent usage). Fourth, Response Surface Methodology (RSM) is applied to generate a set of proxy models to approximate the non-linear relationship between the decision variables and the objective functions and to reduce the overall computational time. Finally, three Multi-Objective Evolutionary Algorithms (MOEAs) are applied to search and compare the optimal sets of decision parameters. The study showed that the SAS process is sensitive to the shale barrier distribution, and that impact is strongly dependent on the location and length of a specific shale barrier. When a shale barrier is located near the injector well, pressure and temperature may build up in the near-well area, preventing additional steam and solvent be injected and, consequently, reducing the oil production. Operational constraints, such as bottom-hole pressure, steam trap criterion, and bottom-hole gas rate in the producer, are among various critical decision variables examined in this study. A key conclusion is that the optimal operating strategy should depend on the underlying heterogeneity. Although this notion has been alluded to in other previous steam- or solvent-based studies, this paper is the first to utilize a MOO framework for systematically determining a specific optimal strategy for each heterogeneity scenario. With the advancement of continuous downhole fibre-optic monitoring, the outcomes can potentially be integrated into other real-time reservoir characterization and optimization work-flows.

Multi-Objective Optimization with Estimation of Distribution Algorithm in a Noisy Environment

2013 ◽  
Vol 21 (1) ◽  
pp. 149-177 ◽  
Author(s):  
Vui Ann Shim ◽  
Kay Chen Tan ◽  
Jun Yong Chia ◽  
Abdullah Al Mamun
Keyword(s):  
Optimization Problems ◽  
Estimation Of Distribution Algorithm ◽  
Computational Time ◽  
Decision Variable ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Noisy Environment ◽  
Multi Objective ◽  
Noisy Information ◽  
Estimation Of Distribution

Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.

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