Adaptive multimodal continuous ant colony optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima

Differential evolution with an evolution path: a DEEP evolutionary algorithm

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs

A γ\gamma-ray Quasi-Periodic modulation in the Blazar PKS 0301−-243?

We report a nominally high-confidence $\gamma$-ray quasi-periodic modulation in the blazar PKS 0301$-$243. For this target, we analyze its \emph{Fermi}-LAT Pass 8 data covering from 2008 August to 2017 May. Two techniques, i.e., the maximum likelihood optimization and the exposure-weighted aperture photometry, are used to build the $\gamma$-ray light curves. Then both the Lomb-Scargle Periodogram and the Weighted Wavelet Z-transform are applied to the light curves to search for period signals. A quasi-periodicity with a period of $2.1\pm0.3$ yr appears at the significance level of $\sim5\sigma$, although it should be noted that this putative quasi-period variability is seen in a data set barely four times longer. We speculate that this $\gamma$-ray quasi-periodic modulation might be evidence of a binary supermassive black hole.Comment: 9 pages, 8 figures; Accepted for publication in Ap

A maximal clique based multiobjective evolutionary algorithm for overlapping community detection

Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm

Empirical Analysis on the International Competitiveness Of Shannxi Agricultural Product

On the foundation of reviewing the trade status of Shannxi agricultural product, adopting Trade Competitive Index, Revealed Comparative Advantage Index and Competitive Advantage Index, this paper calculates and evaluates the international competitiveness of Shannxi agricultural product from 1997 to 2004. It is found that from TC and CA indexes, Shannxi agriculture product is in an advantage position of international competition, while from the RCA index, it doesn’t have a very strong international advantage. In actual application, according to the trade situation of Shannxi agriculture product, we should consider synthetically the calculation results of these three indexes and develop the competitive advantage on the basis of comparative advantage. The conclusion supplies actual mentalities for promoting the international competitiveness of Shannxi agricultural product. Key words: Empirical analysis, International competitiveness, Shannxi agricultural product Résumé: Sur la base de la rétrospection du statut commercial du produit agricole du Shannxi et en adoptant l’Index compétitif du commerce, l’Index de l’avantage comparatif révélé et l’Index de l’avantage compétitif, cet essai calcule et évalue la compétitivité internaionale du produit agricole du Shannxi de 1997 à 2004. On trouve que, selon le premier et le troisième indexs, le produit agricole du Shannxi occupe une position avantageuse dans la compétition internationale, alors que d’après le deuxième index, il ne possède pas un avantage international très solide. Dans l’application actuelle, conformément à la situation du commerce du produit agricole du Shannxi, on doit considérer synthétiquement les résultats de calculation des trois index et développer l’avantage compétitif sur la base de l’avantage comparatif. La conclusion justifie les mentalités actuelles qui insistent à promouvoir la compétitivité internationale du produit agricole du Shannxi. Mots-Clés: analyse empirique, compétitivité internationale, produit agricole du Shannx

Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO