Performance of infeasibility driven evolutionary algorithm (IDEA) on constrained dynamic single objective optimization problems

Singh, Hemant Kumar; Isaacs, Amitay; Nguyễn, Trung Thành; Ray, Tapabrata; Yao, Xin

doi:10.1109/cec.2009.4983339

Cited by 53 publications

(24 citation statements)

References 14 publications

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“…1) Adaption from static optimization algorithms: Several works adapt static optimization algorithms for solving DCOPs with no or simple modifications [5], [30].…”

Section: B a Survey Of Existing Evolutionary Algorithms On Dcopsmentioning

confidence: 99%

Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

Luo

Yue

2017

IEEE Trans. Evol. Computat.

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Dynamic Constrained Optimization Problems (DCOPs) are difficult to solve because both the objective function and constraints can vary with time. Although DCOPs have drawn attention in recent years, little work has been performed to solve DCOPs with multiple dynamic feasible regions from the perspective of locating and tracking multiple feasible regions in parallel. Moreover, few benchmarks have been proposed to simulate the dynamics of multiple disconnected feasible regions. In this paper, first, the idea of tracking multiple feasible regions, originally proposed by Nguyen and Yao, is enhanced by specifically adopting multiple sub-populations. To this end, the Dynamic Species-based Particle Swam Optimization (i.e., DSPSO), a representative multi-population algorithm, is adopted. Second, an ensemble of locating and tracking feasible regions strategies is proposed to handle different types of dynamics in constraints. Third, two benchmarks are designed to simulate the DCOPs with dynamic constraints. The first benchmark, including two variants of G24 (called G24v and G24w), could control the size of feasible regions. The second benchmark, named Moving Feasible Regions Benchmark (MFRB), is highly configurable. The global optimum of MFRB is calculated mathematically for experimental comparisons. Experimental results on G24, G24v, G24w and MFRB show that the DSPSO with the ensemble of strategies performs significantly better than the original DSPSO and other typical algorithms.

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“…1) Adaption from static optimization algorithms: Several works adapt static optimization algorithms for solving DCOPs with no or simple modifications [5], [30].…”

Section: B a Survey Of Existing Evolutionary Algorithms On Dcopsmentioning

confidence: 99%

Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

Luo

Yue

2017

IEEE Trans. Evol. Computat.

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“…Detectors can also be maintained separately from the search population. In this case, they can be just a fixed point [25], one or a set of random solutions [26,82,98], a regular grid of solutions / set of specifically distributed solutions [65], or a list of found peaks [67,69].…”

Section: Detecting Changes By Re-evaluating Solutionsmentioning

confidence: 99%

Evolutionary Dynamic Optimization: Methodologies

Nguyễn

Yang

Branke

et al. 2013

Studies in Computational Intelligence

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Abstract. In recent years, Evolutionary Dynamic Optimization (EDO) has attracted a lot of research effort and has become one of the most active research areas in evolutionary computation (EC) in terms of the number of activities and publications. This chapter provides a summary of main EDO approaches in solving DOPs. The strength and weakness of each approach and their suitability for different types of DOPs are discussed. Current gaps, challenging issues and future directions regarding EDO methodolgies are also presented.

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“…In order to check MI-GA's intrinsic attributes and the ability of solving DCMO, three representative optimization algorithms from the fields of evolutionary computation and artificial immune systems, i.e., Repair GA (RGA) [1], Infeasibility Driven Evolutionary Algorithm (IDEA) [2] and T-cell Artificial Immune System (simply written as T-cell) [8], are picked up to carry out comparison. Notice that although T-cell itself is designed for static constrained optimization problems, it is a competitive artificial immune system with the abilities of environmental adaptation and strong local and global search, being capable of solving time-varying optimization problems.…”

Section: Experimental Studymentioning

confidence: 99%

“…Although some intelligent techniques are popular in the context of optimization, they expose many faults when directly applied to dynamic multimodal problems, due to robustness, diversity and environmental adaptation. In order to overcome such shortcomings, several types of approaches for dynamic non-constrained multimodal optimization (DNMO) problems, such as evolutionary algorithms [1][2][3] and particle swarm optimization [4], were developed. However, when addressing DCMO, such methods become difficult in simultaneously finding multiple global optimal solutions and rapidly justifying whether the environment changes, owing to the limitation of constraints.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective immune genetic algorithm solving dynamic single-objective multimodal constrained optimization

Zhang

Liao

Wang

2012

2012 8th International Conference on Natural Computation

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This work investigates one multi-objective immune genetic algorithm to solve dynamic constrained single-objective multimodal optimization problems in terms of the concept of constraint-dominance and biological immune inspirations. The algorithm assumes searching multiple global optimal solutions along diverse searching directions, by means of the environmental detection and two evolving subpopulations. It exploits various kinds of promising regions through executing the periodical suppression mechanism and periodically adjusting the mutation magnitude. The sufficient diversity of population can be maintained relying upon a dynamic suppression index, and meanwhile the high-quality solutions can be found rapidly during the process of solution search. Comparative experiments show that the proposed approach can not only outperform the compared algorithms, but also rapidly acquire the global optima in each environment for each test problem, and thus it is a competitive optimizer.

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Performance of infeasibility driven evolutionary algorithm (IDEA) on constrained dynamic single objective optimization problems

Cited by 53 publications

References 14 publications

Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

Continuous Dynamic Constrained Optimization With Ensemble of Locating and Tracking Feasible Regions Strategies

Evolutionary Dynamic Optimization: Methodologies

Multi-objective immune genetic algorithm solving dynamic single-objective multimodal constrained optimization

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