Elite opposition-based flower pollination algorithm

Zhou, Yongquan; Wang, Rui; Luo, Qifang

doi:10.1016/j.neucom.2015.01.110

Cited by 123 publications

(43 citation statements)

References 38 publications

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“…Moreover, FPA can easily solve low-dimensional unimodal optimization problems. Whereas when handling the high-dimensional and multi-modal optimization problems, we can clearly discover that the solutions obtained by FPA are not good enough [61]. Thus, in order to improve the performance of basic FPA, a modified FPA is proposed, which could be demonstrated as i) PSO in local update strategy (PSO-LUS) is applied to the local pollination process, and ii) dynamic switching probability strategy (DSPS) is used to obtain the better balance between exploration and exploitation process.…”

Section: Two New Search Strategiesmentioning

confidence: 91%

Uncertain portfolio selection with high-order moments

Chen

Wang

Jun

et al. 2017

IFS

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In the mean-variance-skewness-kurtosis framework, this paper discusses an uncertain higher-order moment portfolio selection problem when security returns are given by experts' evaluations. Based on uncertainty theory and the assumption that the security returns are zigzag uncertain variables, an uncertain multi-objective portfolio optimization model is proposed by considering the maximization of both the expected return and skewness of portfolio return while simultaneously minimizing the risk and kurtosis of portfolio return. Subsequently, the proposed model is transformed into a single-objective programming model by using fuzzy programming approach, in which investor preferences for high moments are incorporated. Furthermore, a modified flower pollination algorithm (MFPA) is developed for solution, in which PSO in local update strategy (PSOLUS) and dynamic switching probability strategy (DSPS) are employed to enhance the local searching and global searching abilities. Finally, a numerical example is presented to illustrate the application of the proposed model and solution comparisons are also given to demonstrate the effectiveness of the designed algorithm.

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Section: Two New Search Strategiesmentioning

confidence: 91%

Uncertain portfolio selection with high-order moments

Chen

Wang

Jun

et al. 2017

IFS

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“…In fact, it has recently been proved that FPA can have guaranteed global convergence under the right conditions [20]. FPA has been applied to many applications with an expanding literature [1,4,38,63].…”

Section: Algorithms Based On Swarm Intelligencementioning

confidence: 99%

Swarm intelligence: past, present and future

Yang¹,

et al. 2017

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Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.

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“…It has been proved that an opposite candidate solution has a higher chance to be closer to the global optimal solution than a random candidate solution [47]. Elite oppositionbased learning (EOBL) [48] is based on the elite step leader using OBL principle to generate elite opposition-based population to participate in competitive evolution, so as to improve population diversity of LSA. The step leader with the best fitness value is defined as elite step leader = ( ,1 , ,2 , .…”

Section: Elite Opposition-based Learning (Eobl)mentioning

confidence: 99%

A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization

Zhou

Xiu-li

2017

Discrete Dynamics in Nature and Society

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In this paper, a novel hybrid lightning search algorithm-simplex method (LSA-SM) is proposed to solve the shortcomings of lightning search algorithm (LSA) premature convergence and low computational accuracy and it is applied to function optimization and constrained engineering design optimization problems. The improvement adds two major optimization strategies. Simplex method (SM) iteratively optimizes the current worst step leaders to avoid the population searching at the edge, thus improving the convergence accuracy and rate of the algorithm. Elite opposition-based learning (EOBL) increases the diversity of population to avoid the algorithm falling into local optimum. LSA-SM is tested by 18 benchmark functions and five constrained engineering design problems. The results show that LSA-SM has higher computational accuracy, faster convergence rate, and stronger stability than other algorithms and can effectively solve the problem of constrained nonlinear optimization in reality.

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Elite opposition-based flower pollination algorithm

Cited by 123 publications

References 38 publications

Uncertain portfolio selection with high-order moments

Uncertain portfolio selection with high-order moments

Swarm intelligence: past, present and future

A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization

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