A Review of Features and Limitations of Existing Scalable Multiobjective Test Suites

Zapotecas-Martínez, Saúl; Coello, Carlos A. Coello; Aguirre, Hernán; Tanaka, Kiyoshi

doi:10.1109/tevc.2018.2836912

Cited by 52 publications

(20 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There exist many multi-objective benchmark problems in the literature [57], but most of them do not concern the sparsity of Pareto optimal solutions. For example, the DTLZ test suite [58] In spite of some sparse MOPs in existing multiobjective test suites, most of them do not have a suitable hardness to study the performance of MOEAs for sparse MOPs.…”

Section: A Sparse Mops In Existing Test Suitesmentioning

confidence: 99%

An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

Tian

Zhang

Wang

et al. 2020

IEEE Trans. Evol. Computat.

287

View full text Add to dashboard Cite

In the last two decades, a variety of different types of multi-objective optimization problems (MOPs) have been extensively investigated in the evolutionary computation community. However, most existing evolutionary algorithms encounter difficulties in dealing with MOPs whose Pareto optimal solutions are sparse (i.e., most decision variables of the optimal solutions are zero), especially when the number of decision variables is large. Such large-scale sparse MOPs exist in a wide range of applications, for example, feature selection that aims to find a small subset of features from a large number of candidate features, or structure optimization of neural networks whose connections are sparse to alleviate overfitting. This paper proposes an evolutionary algorithm for solving large-scale sparse MOPs. The proposed algorithm suggests a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration, to ensure the sparsity of the generated solutions. Moreover, this paper also designs a test suite to assess the performance of the proposed algorithm for large-scale sparse MOPs. Experimental results on the proposed test suite and four application examples demonstrate the superiority of the proposed algorithm over seven existing algorithms in solving large-scale sparse MOPs.

show abstract

Section: A Sparse Mops In Existing Test Suitesmentioning

confidence: 99%

An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

Tian

Zhang

Wang

et al. 2020

IEEE Trans. Evol. Computat.

287

View full text Add to dashboard Cite

show abstract

“…[2,32] Independent parameters Parameters in each function that independently adjust the challenges presented to the DM regarding to the convergence and coverage. [11,19] No extremal or medial parameters Both are to prevent exploitation by truncation, based on correction operators in the case of extremal parameters and on intermediate recombination in the case of medial parameters. [14] Bias…”

Section: Modalitymentioning

confidence: 99%

“…In addition to the constraints presented in Eqs. (19), (20) and (21), it is possible to select large regions in the objective space in the following way: consider a problem where the objective space is located in the first orthant of R M space. In this case, consider the angle θ i between the point y = F (x) and the vectors of the canonical basis e i = (0, .…”

Section: Equality and Inequality Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Scalable and customizable benchmark problems for many-objective optimization

Meneghini

Alves

Gaspar‐Cunha

et al. 2020

Applied Soft Computing

View full text Add to dashboard Cite

Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective optimization (MOO) field. One way to measure algorithm performance is through the use of benchmark functions (also called test functions or test suites), which are artificial problems with a well-defined mathematical formulation, known solutions and a variety of features and difficulties. In this paper we propose a parameterized generator of scalable and customizable benchmark problems for MaOPs. It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features. We propose here the concept of generative benchmarking, in which one can generate an infinite number of MOO problems, by varying parameters that control specific features that the problem should have: scalability in the number of variables and objectives, bias, deceptiveness, multimodality, robust and non-robust solutions, shape of the Pareto front, and constraints. The proposed Generalized Position-Distance (GPD) tunable benchmark generator uses the position-distance paradigm, a basic approach to building test functions, used in other benchmarks such as Deb, Thiele, Laumanns and Zitzler (DTLZ), Walking Fish Group (WFG) and others. It includes scalable problems in any number of variables and objectives and it presents Pareto fronts with different characteristics. The resulting functions are easy to understand and visualize, easy to implement, fast to compute and their Pareto optimal solutions are known.

show abstract

“…There are two general methods to solve the multiobjective optimization problem: weight-based method and Pareto-based method [33][34][35]. From Theorems 9 and 10, we know that reducing the PPL and the VEF can improve the performance of GMDR and GMD under certain conditions.…”

Section: Fitness Functionmentioning

confidence: 99%

An Energy-Balanced Path Planning Algorithm for Multiple Ferrying UAVs Based on GA

Wang

Zhang

Deng³

et al. 2020

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

When performing a search and rescue mission, unmanned aerial vehicles (UAVs) should continuously search targets above the mission area. In order to transfer the search and rescue information quickly and efficiently, two types of UAVs, the ferrying UAVs and the searching UAVs, are used to complete the mission. Obviously, this application scenario requires an efficient path planning method for ferrying UAVs. The existing path planning methods for ferrying UAVs usually focus on shortening the path length and ignore the different initial energy of ferrying UAVs. However, the following problem does exist: if the ferrying UAV with less initial energy is assigned a longer path, meaning that the ferrying UAV with less initial energy will ferry messages for more searching UAVs. When the lower-initial-energy ferrying UAV is running out of energy, more searching UAVs will no longer deliver messages successfully. Therefore, the mismatch between the planned path length and the initial energy will eventually result in a lower global message delivery ratio. To solve this problem, we propose a new concept energy-factor for a ferrying UAV and use the variance of all ferrying UAVs’ energy-factor to measure the balance between the planned path length and the initial energy. Further, we model the energy-balanced path-planning problem for multiple ferrying UAVs, which actually is a multiobject optimization problem of minimizing the planned path length and minimizing the variance of all ferrying UAVs’ energy-factor. Based on the genetic algorithm, we design and implement an energy-balanced path planning algorithm (EMTSPA) for multiple ferrying UAVs to solve this multiobject optimization problem. Experimental results show that EMTSPA effectively increases the global message delivery ratio and decreases the global message delay.

show abstract

A Review of Features and Limitations of Existing Scalable Multiobjective Test Suites

Cited by 52 publications

References 64 publications

An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

Scalable and customizable benchmark problems for many-objective optimization

An Energy-Balanced Path Planning Algorithm for Multiple Ferrying UAVs Based on GA

Contact Info

Product

Resources

About