On evolution strategy optimization in dynamic environments

Weicker, Karsten; Weicker, Nicole

doi:10.1109/cec.1999.785525

Cited by 31 publications

(32 citation statements)

References 8 publications

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“…Differently, other researchers want to observe "how close to the moving optimum a solution found by an algorithm is" [37,38]. Therefore, the measurements require that the global optimum value is known during the dynamic changes, e.g., the offline error [39], average score [40], accuracy [41] and other measurements based on the distance between the solutions found by the algorithm and the global optimum [42,43]. Others are concerned for measures which can characterize the population as a whole, e.g., the average performance or the average robustness [44].…”

Section: Measurementsmentioning

confidence: 99%

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

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

492

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: Measurementsmentioning

confidence: 99%

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

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

492

213

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“…Previous studies on the behavior of evolution strategies in tracking dynamic optimums can be found in [2,25,1]. The standard evolution strategy and the ES with the covariance matrix adaptation have been considered.…”

Section: Behavior Of Evolution Strategies In Dynamic Optimizationmentioning

confidence: 99%

“…Adapt the strategy parameters of the evolutionary algorithms [7,15]. However, conventional self-adaptation can have negative influences if no particular attention is paid to the dynamics of the optimums [2,25].…”

Section: Introductionmentioning

confidence: 99%

On Test Functions for Evolutionary Multi-objective Optimization

Okabe

Jin

Olhofer

et al. 2004

Lecture Notes in Computer Science

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Abstract. Dynamic optimization using evolutionary algorithms is receiving increasing interests. However, typical test functions for comparing the performance of various dynamic optimization algorithms still lack. This paper suggests a method for constructing dynamic optimization test problems using multi-objective optimization (MOO) concepts. By aggregating different objectives of an MOO problem and changing the weights dynamically, we are able to construct dynamic single objective and multi-objective test problems systematically. The proposed method is computationally efficient, easily tunable and functionally powerful. This is mainly due to the fact that the proposed method associates dynamic optimization with multi-objective optimization and thus the rich MOO test problems can easily be adapted to dynamic optimization test functions.

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“…In a deterministic setting (e.g., where the experiment is a simulation without randomness) even the individual with the highest fitness will have zero fitness when it is evaluated a second time (cf. [14,15]). …”

Section: Adaptive Population Sizementioning

confidence: 99%

Self-Adaptive Scouting—Autonomous Experimentation for Systems Biology

Matsumaru

Centler

Zauner

et al. 2004

Lecture Notes in Computer Science

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Abstract. We introduce a new algorithm for autonomous experimentation. This algorithm uses evolution to drive exploration during scientific discovery. Population size and mutation strength are self-adaptive. The only variables remaining to be set are the limits and maximum resolution of the parameters in the experiment. In practice, these are determined by instrumentation. Aside from conducting physical experiments, the algorithm is a valuable tool for investigating simulation models of biological systems. We illustrate the operation of the algorithm on a model of HIVimmune system interaction. Finally, the difference between scouting and optimization is discussed.

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On evolution strategy optimization in dynamic environments

Cited by 31 publications

References 8 publications

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

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

On Test Functions for Evolutionary Multi-objective Optimization

Self-Adaptive Scouting—Autonomous Experimentation for Systems Biology

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