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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.

Topological semimetals feature a diversity of nodal manifolds including nodal points, various nodal lines and surfaces, and recently novel quantum states in non-Hermitian systems have been arousing widespread research interests. In contrast to Hermitian systems whose bulk nodal points must form closed manifolds, it is fascinating to find that for non-Hermitian systems exotic nodal manifolds can be bounded by exceptional points in the bulk band structure. Such exceptional points, at which energy bands coalesce with band conservation violated, are iconic for non-Hermitian systems. In this work, we show that a variety of nodal lines and drumheads with exceptional boundary can be realized on 2D and 3D honeycomb lattices through natural and physically feasible non-Hermitian processes. The bulk nodal Fermi-arc and drumhead states, although is analogous to, but should be essentially distinguished from the surface counterpart of Weyl and nodal-line semimetals, respectively, for which surface nodal-manifold bands eventually sink into bulk bands. Then we rigorously examine the bulkboundary correspondence of these exotic states with open boundary condition, and find that these exotic bulk states are thereby undermined, showing the essential importance of periodic boundary condition for the existence of these exotic states. As periodic boundary condition is non-realistic for real materials, we furthermore propose a practically feasible electrical-circuit simulation, with non-Hermitian devices implemented by ordinary operational amplifiers, to emulate these extraordinary states.

Nature has provided rich models for computational problem solving, including optimizations based on the swarm intelligence exhibited by fireflies, bats, and ants. These models can stimulate computer scientists to think nontraditionally in creating tools to address application design challenges.Most if not all engineering tasks involve decisions about the product, service, and system design, which are related in some way to optimizing time and resources as well achieving balance between maximizing performance, profit, sustainability, quality, safety, and efficiency and minimizing cost, energy consumption, defects, and environmental impact. As the sidebar "Elements of an Optimization Problem" describes, many of these design problems have multiple objectives bound by highly complex constraints. The traditional approach of specializing design method to problem type does not fit well with complex, nonlinear problems, such as multicriteria engineering designs and multiple complex feature extraction in big data.This lack of suitability has motivated interest in more novel optimization approaches. One emerging trend is to combine heuristic search with multiagent systems to solve real-world business and engineering problems. 1 Such a combination has accuracy, efficiency, and performance advantages over specialized methods. For example, it can be used to more efficiently and accurately optimize or tune parameters in artificial neural networks, which are essential to many AI tasks.One class of novel optimization algorithms is based on swarm intelligence (SI). SI captures the idea that decision making among organisms in a community, such as ants and bees, uses local information and interactions with other agents and with their own environment, which in turn could be responsible for the rise of collective or social intelligence. One hypothesis is that complex interactions directly or indirectly contribute to the emergence of intelligence in highly developed biological species. The reasoning is that biological change results from the organism responding and adapting to alterations in its community and environment. Groups of

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