Benchmarks for dynamic multi-objective optimisation algorithms

Helbig, Mardé; Engelbrecht, Andries P.

doi:10.1145/2517649

Cited by 65 publications

(38 citation statements)

References 59 publications

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“…Recently, benchmark generators for continuous dynamic constrained optimization [77,78,26,14] and continuous dynamic multiobjective optimization [25,61,79,80,81,82,83,84,85] are proposed. But, constrained and multi-objective optimization under the discrete space has not attracted much attention yet and deserves future consideration.…”

Section: The Generation Of Dynamicsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

472

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: The Generation Of Dynamicsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

472

213

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“…First, we study each test problem and we determine some restrictions on the search space Helbig (2014). Then, we run NSGA-II with 1000 generations and with a population size equal to 2000.…”

Section: Optimal Data Sets Generationmentioning

confidence: 99%

“…However, it was not until the late 1980s that the subject received the interest of many researchers. Although many other optimization techniques have been adapted to dynamic environments such as particle swarm optimization (Wei et al 2013;Helbig 2014;Blackwell et al 2006) and artificial immune systems (Shang et al 2014;Zhang 2008), the EA area is still the largest one. DOPs include dynamic single-objective optimization problems (DSOPs) and dynamic multi-objective optimization problems (DMOPs).…”

Section: Introductionmentioning

confidence: 99%

A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

2015

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In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve dynamic optimization problems has obtained great attention among many researchers. However, most of works are restricted to the single-objective case. In this work, we propose an adaptive hybrid population management strategy using memory, local search and random strategies, to effectively handle environment dynamicity for the multi-objective case where objective functions change over time. Moreover, the proposed strategy is based on a new technique that detects the change severity, according to which it adjusts the number of memory and random solutions to be used. This ensures, on the one hand, a high level of convergence and on the other hand, the required diversity. We propose a dynamic version of the Non dominated Sorting Genetic Algorithm II, within which we integrate the above-mentioned strategies. Empirical results show that our proposal based on the use of the adaptive strategy is able to handle dynamic environments and to track the Pareto front as it changes over time. Moreover, when confronted with several recently proposed dynamic algorithms, it has presented competitive and better results on most problems.Keywords Change severity sensing · Dynamic multiobjective optimization · Time-changing objective functions · Adaptive population management · Memory-based strategy · Local search-based strategy · Change severity-based strategy

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“…As an example, we just use the simple F 3 defined in Eq. (6) to illustrate dynamism in a dynamic environment.…”

Section: A the Pof-associated Componentmentioning

confidence: 99%

“…Recently, Helbig and Engelbrecht [6] have made a sound investigation into the current DMOPs used in the literature, and have proposed characteristics that an ideal DMO benchmark function suite should exhibit. Besides, after highlighting shortcomings of current DMOPs, they also provided several benchmark functions with complicated POSs and with either an isolated or deceptive POF.…”

Section: Introductionmentioning

confidence: 99%

A framework of scalable dynamic test problems for dynamic multi-objective optimization

Jiang

Yang

2014

2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

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Dynamic multi-objective optimization has received increasing attention in recent years. One of striking issues in this field is the lack of standard test suites to determine whether an algorithm is capable of solving dynamic multi-objective optimization problems (DMOPs). So far, a large proportion of test functions commonly used in the literature have only two objectives. It is greatly needed to create scalable test problems for developing algorithms and comparing their performance for solving DMOPs. This paper presents a framework of constructing scalable dynamic test problems, where dynamism can be easily added and controlled, and the changing Pareto-optimal fronts are easy to understand and their landscapes are exactly known. Experiments are conducted to compare the performance of four state-of-the-art algorithms on several typical test functions derived from the proposed framework, which gives a better understanding of the strengths and weaknesses of these tested algorithms for scalable DMOPs.

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Benchmarks for dynamic multi-objective optimisation algorithms

Cited by 65 publications

References 59 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

A framework of scalable dynamic test problems for dynamic multi-objective optimization

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