Evolutionary dynamic optimization: A survey of the state of the art

Nguyễn, Trung Thành; Yang, Shengxiang; Branke, Juergen

doi:10.1016/j.swevo.2012.05.001

Cited by 558 publications

(318 citation statements)

References 74 publications

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“…Comprehensive surveys can be found in [13,14]. Probably the most commonly used benchmark generators for DOPs are: (1) the moving peaks benchmark (MPB) [4]; (2) the generalized dynamic benchmark generator (GDBG) [40]; (3) the exclusive-or (XOR) DOP generator for binary-encoded problems [70]; and (4) the dynamic benchmark generator for permutation-encoded problems (DBGP) [71].…”

Section: The Generation Of Dynamicsmentioning

confidence: 99%

“…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%

“…However, it has been a growing interest to apply SI algorithms on different DOPs. EDO has received extensive attention with several surveys [13,12,14,15] and books [16,17,18,19,20], whereas SI dynamic optimization (SIDO) has not received much attention, with exception of some very brief reviews of PSO in [14] and ACO in [15] included as subsections in the EDO surveys. The aim of this paper is to extend these reviews of ACO and PSO and provide a comprehensive survey of existing work done related to SIDO, which also includes the less popular and recent SI algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

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474

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%

Section: The Generation Of Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

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474

213

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“…Many applications of evolutionary algorithms on dynamic problems are considered in literature [1,13], and there are runtime analyses building on theoretical studies of evolutionary algorithms for dynamic problems [4,7,15]. The utility of a population for tracking problems was studied in evolutionary computation by Jansen and Schellbach [7], while different mechanisms for ensuring population diversity have been considered by Oliveto and Zarges [14].…”

Section: Introductionmentioning

confidence: 99%

MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions

Lissovoi

Witt

2015

Algorithmica

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We study the behavior of a population-based EA and the Max-Min Ant System (MMAS) on a family of deterministically-changing fitness functions, where, in order to find the global optimum, the algorithms have to find specific local optima within each of a series of phases. In particular, we prove that a (2+1) EA with genotype diversity is able to find the global optimum of the Maze function, previously considered by Kötzing and Molter (PPSN 2012, 113-122), in polynomial time. This is then generalized to a hierarchy result stating that for every µ, a (µ+1) EA with genotype diversity is able to track a Maze function extended over a finite alphabet of µ symbols, whereas population size µ − 1 is not sufficient. Furthermore, we show that MMAS does not require additional modifications to track the optimum of the finitealphabet Maze functions, and, using a novel drift statement to simplify the analysis, reduce the required phase length of the Maze function.

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“…They usually ensure adaptability by maintaining diversity in the population, explicitly increasing diversity after a change has been detected, or dividing the population into several subpopulations to simultaneously explore and track several promising regions in the search space. Recent surveys on this topic can be found in Jin & Branke (2005); Nguyen et al (2012) …”

Section: Dynamic Optimization Problemsmentioning

confidence: 99%

An Overview on Evolutionary Algorithms

Data Mining Using Grammar Based Genetic Programming and Applications

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Evolutionary dynamic optimization: A survey of the state of the art

Cited by 558 publications

References 74 publications

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

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

MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions

An Overview on Evolutionary Algorithms

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