Designing benchmark problems for large-scale continuous optimization

Omidvar, Mohammad Nabi; Li, Xiaodong; Tang, Ke

doi:10.1016/j.ins.2014.12.062

Cited by 114 publications

(70 citation statements)

References 51 publications

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“…Several important observations can be made with regard to separability, non-separability and partial separability [44], which are listed below.…”

Section: A Basic Concepts In Large-scale Optimizationmentioning

confidence: 99%

“…Characteristic (1) and characteristic (3) follow the design principles of large-scale single-objective optimization problems, as recommended in [44]; characteristic (4) follows the suggestions in [27] and [28] to take into consideration the variable linkages on the PSs, while characteristic (2) is to reflect the scenarios in real-world conceptual design [50]- [52].…”

Section: Test Problem Characteristicsmentioning

confidence: 99%

“…Despite the fact that largescale optimization has attracted much attention in singleobjective optimization [35]- [37], little work has been reported on large-scale multi-and many-objective optimization with few exceptions [38]- [40]. This can partly be attributed to the lack of large-scale multi-and many-objective optimization test problems, unlike the rich literature on large-scale singleobjective optimization [41]- [44].…”

Section: Introductionmentioning

confidence: 99%

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Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

Cheng

Jin

Olhofer

et al. 2017

IEEE Trans. Cybern.

275

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Abstract-The interests in multi-and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multi-and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multi-and many-objective optimization problems may involve a large number of decision variables. As has been evident in the history of evolutionary optimization, the development of evolutionary algorithms for solving a particular type of optimization problems has undergone a co-evolution with the development of test problems. To promote the research on large-scale multi-and many-objective optimization, we propose a set of generic test problems based on design principles widely used in the literature of multi-and many-objective optimization. In order for the test problems to be able to reflect challenges in real-world applications, we consider mixed separability between decision variables and non-uniform correlation between decision variables and objective functions. To assess the proposed test problems, six representative evolutionary multi-and manyobjective evolutionary algorithms are tested on the proposed test problems. Our empirical results indicate that although the compared algorithms exhibit slightly different capabilities in dealing with the challenges in the test problems, none of them are able to efficiently solve these optimization problems, calling for the need for developing new evolutionary algorithms dedicated to large-scale multi-and many-objective optimization.

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“…Several important observations can be made with regard to separability, non-separability and partial separability [44], which are listed below.…”

Section: A Basic Concepts In Large-scale Optimizationmentioning

confidence: 99%

Section: Test Problem Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

Cheng

Jin

Olhofer

et al. 2017

IEEE Trans. Cybern.

275

View full text Add to dashboard Cite

show abstract

“…(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14), and named the interference minimization based TPC model (IM-TPC). According to the definition of large-scale problems [24], the scale of a TPC model is influenced by (1) the size of a target industrial indoor environment (which is linked to the number of APs), (2) the grid size (gs), (3) the number of dominant obstacles, and (4) .  The first factor determines the dimensionality of a search space and impacts the complexity of fitness evaluation.…”

Section: Interferencementioning

confidence: 99%

“…Large-scale problems are characterized in at least one of the following dimensions [24]. Firstly, the search space exponentially grows with the increasing number of decision variables.…”

Section: Introductionmentioning

confidence: 99%

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

Gong

David

Tanghe

et al. 2018

Applied Soft Computing

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The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), it becomes an unavoidable challenge to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To tackle this challenge, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small-and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC according to varying target coverage percentage as well as number and placement direction of dominant obstacles.

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A kind of epistasis‐tunable test functions for genetic algorithms

Zhong

Zhu

2018

Concurrency and Computation

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Summary Genetic algorithm (GA) is one of the most popular algorithms of evolutionary computation. To evaluate the performance of GAs, test functions with different levels of epistasis have been included in most of the commonly used benchmark platforms. Such test functions have been produced in general by assuming an underlying linear model for the fitness of a string, in which variable interaction should be known beforehand. This paper proposes to compose epistasis‐tunable test functions via linear combinations of simple basis functions so as to avoid explicit dependence on the knowledge of variable interaction. It is remarked that, for a GA with a binary encoding, a linearly separable fitness function, ie, a zero‐epistasis fitness function, can be decomposed into a superposition of periodical basis functions whose frequencies are exponential to 2. A kind of epistasis‐tunable test functions is accordingly produced by summing up sinusoidal basis functions with such frequencies and proper phases. The merits of thus constructed test functions are: Both the locations and values of the global maxima are trivially known; their degrees of epistasis are smoothly tunable simply by changing the locations of the global maxima. Finally, illustration examples are studied, and the results confirm the claims about the merits.

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Designing benchmark problems for large-scale continuous optimization

Cited by 114 publications

References 51 publications

Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

A kind of epistasis‐tunable test functions for genetic algorithms

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