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Abstract
In order to well solve the phase-only reconfigurable arrays synthesis problems, we introduce an adaptive strategy in invasive weed optimization (IWO), and integrate the adaptive IWO (AIWO) into the framework of MOEA/D, a popular multi-objective algorithm. Then, a new version of MOEA/D with adaptive IWO, named MOEA/D-AIWO is proposed in this paper for solving the synthesis problems. In MOEA/D-AIWO, the proposed adaptive strategy is adopted for improving search ability and balancing diversity and convergence. We introduce an adaptive standard deviation, which changes not only with the increase of evolution generations, but also exponentially with the fitness function value of each individual. This strategy improves the convergence rate and helps the seeds escape from local optimum. Taking advantage of the powerful searching ability of invasive weeds and well framework of MOEA/D, the overall performance of the proposed MOEA/D-AIWO is illustrated in solving two sets of phase-only reconfigurable arrays synthesis problems. Comparing results with MOEA/D-IWO (MOEA/D with original IWO) and MOEA/D-DE are also provided in this paper.
Highlights
A In many actual applications such as satellite communications and radar navigations, single antenna array is generallyD required to have the capability of producing a number of radiation patterns with different shapes, so as to save spaceE and reduce cost
Ue obtained by Multi-objective evolutionary algorithm based on decomposition (MOEA/D)-adaptive IWO (AIWO) is 45o, equal to that obtained by MOEA/D-invasive weed optimization (IWO), while the value obtained by MOEA/D-differential evolution (DE) is 53°, which is 8° wider than those obtained by MOEA/D-AIWO and MOEA/D-IWO
The SLL values obtained by MOEA/D-IWO and MOEA/D-DE are -20.5452 dB and -20.4597 dB, which are 0.375 dB and 0.4605 dB higher than that obtained by MOEA/D-AIWO respectively
Summary
A In many actual applications such as satellite communications and radar navigations, single antenna array is generally. In order to solve the design problem of phase-only reconfigurable arrays effectively, a new version of MOEA/D with an adaptive IWO, named MOEA/D-AIWO is proposed. Spatial Dispersion: The produced seeds in this step are being dispread over the search space by normally It can be seen from Eq (2) that, the adaptive standard deviation of the weed stditer changes exponentially with its fitness value, and the higher the fitness value, the smaller standard deviation the weed will have, which enables the seeds distribute near around their better parents, and far away from their worse parents relatively. The Open Chemical Engineering Journal, 2015, Volume 9 127 younger generations likely to be larger than that in the older generations This will help the new produced seeds escape from local optimum, improve the convergence rate, and balance the global and local search capabilities effectively at the same time.
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