A hybrid evolutionary computation algorithm for global optimization

Bashir, Hassan A.; Neville, Richard

doi:10.1109/cec.2012.6252892

Cited by 8 publications

(2 citation statements)

References 89 publications

(236 reference statements)

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“…In [22], this hybrid method is further developed to solve general constrained optimization problems. In [29], evolutionary computation (EC) algorithms are combined with a sequential quadratic programming (SQP) algorithm to solve constrained global optimization problems. The hybrid methods mentioned above have better numerical performances when compared with pure stochastic search methods.…”

Section: Q2mentioning

confidence: 99%

A hybrid approach to constrained global optimization

Liu

Zhang²,

et al. 2016

Applied Soft Computing

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a b s t r a c tIn this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of these box constrained optimization problems, two hybrid methods are introduced, where two different strategies are used to combine limited memory BFGS (L-BFGS) with Greedy Diffusion Search (GDS). The convergence issue of the two hybrid methods is addressed. To evaluate the effectiveness of the proposed algorithm, 18 box constrained and 4 general constrained problems from the literature are tested. Numerical results obtained show that our proposed hybrid algorithm is more effective in obtaining more accurate solutions than those compared to.

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Section: Q2mentioning

confidence: 99%

A hybrid approach to constrained global optimization

Liu

Zhang²,

et al. 2016

Applied Soft Computing

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“…We must remark here that this threshold parameter is user defined and its appropriate value is determined empirically, preliminary investigations reveal that a value of σq jk ≤ 0.01 is suitable for crossover and mutation probabilities of P c = 1.0 and P m = 1/l respectively (see [15] for details). Nonetheless, since determining optimum value for this threshold parameter relies on some other EC parameters, a thorough sensitivity analysis will be conducted in the future.…”

Section: A the Proposed Convergence Threshold Parametermentioning

confidence: 99%

Convergence measurement in evolutionary computation using Price's theorem

Bashir

Neville

2012

2012 IEEE Congress on Evolutionary Computation

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Abstract-Evolutionary computations are naturally inspired stochastic algorithms that are capable of running perpetually. When deployed as optimization tools, it is imperative to prescribe a set of definitive stopping criteria that if satisfied, the evolutionary process could be brought to a halt. User specified limits on maximum evaluations or generations are the common measures used to stop the evolution due to resource constraints that might directly/indirectly be imposed on the system. Conversely, we propose a novel convergence detection mechanism that monitors the contribution of the genetic operators on the fitness progress and the diversity profile of the population via the ±σ crossover envelope. This adaptively terminates the evolution as convergence sets in. Extended Price's theorem is utilized to estimate the dynamical contributions of the individual genetic operators. Experimental results show that under standard parameter settings with binary tournament selection, the proposed technique is robust and could be a promising alternative to the conventional similarity measure-based methods for convergence detection.

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