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The vortex search is proposed as a new optimization algorithm recently. This algorithm has the advantages of simple operation and strong search capabilities. By introducing quantum computing into this algorithm, A quantum-inspired vortex search algorithm is presented in this paper. The initial population of the algorithm has only one individual called vortex center. First this individual is encoded by qubits described on the Bloch sphere, and then by repeatedly rotating all qubits on this individual about the same coordinate axis through random angles, some new individuals are generated. By choosing the best individual as a new vortex center, and rotating it again until meeting the termination conditions, the global optimal solution can be obtained. As the search in each dimension is carried out on the Bloch sphere, thus it is helpful to enhance the diversity of candidate solutions and inhibit premature convergence in the late stages of the algorithm. That the proposed algorithm is superior to the original one is demonstrated by the experimental results of some benchmark functions extreme optimization.

This article investigates insertion–deletion systems of small size, where at most two symbols can be used in the description of insertion or deletion rules in a context-free or contextual manner. The basic result shows a characterization by context-free grammars of insertion–deletion systems, which insert or delete one symbol in one symbol left context (systems of size (1, 1, 0; 1, 1, 0)). If context-free insertion or deletion rules are considered (systems of size (2, 0, 0; 1, 1, 0) or (1, 1, 0; 2, 0, 0)), then we show that corresponding systems are not computationally complete. However, if the insertion and the deletion operations having same size as above are considered in the distributed framework of P systems, then the computational power strictly increases and the obtained models become computationally complete. The article also shows that if context-free insertion and deletion rules of two symbols (of size (2, 0, 0; 2, 0, 0)) are used in combination with P systems, then the obtained model is still not computationally complete. Finally some open problems are presented.

The parity problem is a well-known benchmark task in various areas of computer science. Here we consider its version for one-dimensional, binary cellular automata, with periodic boundary conditions: if the initial configuration contains an odd number of 1s, the lattice should converge to all 1s; otherwise, it should converge to all 0s. Since the problem is ill-defined for even-sized lattices (which, by definition, would never be able to converge to 1), it suffices to account for odd-sized lattices only. We are interested in determining the minimal neighbourhood size that allows the problem to be solvable for any arbitrary initial configuration. On the one hand, we show that radius 2 is not sufficient, proving that there exists no radius 2 rule that can solve the parity problem, even in the simpler case of prime-sized lattices. On the other hand, we design a radius 4 rule that converges correctly for any initial configuration and formally prove its correctness. Whether or not there exists a radius 3 rule that solves the parity problem remains an open problem; however, we review recent data against a solution in radius 3, thus providing strong empirical evidence that there may not exist a radius 3 solution even for prime-sized lattices only, contrary to a recent conjecture in the literature.

As an important administrative task in the area of education, course timetabling is a complex optimization problem that is difficult to solve by conventional methods. The paper adapts a new nature-inspired metaheuristic called ecogeography-based optimization (EBO), which enhances biogeography-based optimization by equipping the population with a neighborhood structure and designing two new migration operators named global migration and local migration, to solve the university course timetabling problem (UCTP). In particular, we develop two discrete migration operators for efficiently evolving UCTP solutions based on the principle of global and local migration in EBO, and design a repair process for effectively coping with infeasible timetables. We test the discrete EBO algorithm on a set of problem instances from four universities in China, and the experimental results show that the proposed method exhibits a promising performance advantage over a number of state-of-the-art methods.

Amorphous computing presents a novel computational paradigm. The respective computational models have been recently introduced and studied in a series of works by J. Wiedermann and his Ph.D. student L. Petrů. From a computational viewpoint, amorphous computing systems differ from the classical ones almost in every aspect: they consist of a set of tiny, independent and self-powered processors or robots that can communicate wirelessly to a limited distance. The processors are randomly placed in a closed area or volume and form an ad-hoc network; in some applications they can move, either actively, or passively (e.g., in a bloodstream). Assuming the exponential progress in all sciences resulting in our ability to produce amorphous computing systems with myriads of processors, an unmatched application potential is expected profoundly to change all areas of science and life. But prior to this state of the matters theoretical and practical studies of the computational properties and efficiency of amorphous computing systems must be performed. It is expected that an indispensable part of computer science will be affected by this trend.

Aiming to solve the problem of instance-intensive workflow scheduling in private cloud environment, this paper first formulates a scheduling optimization model considering the communication time between tasks. The objective of this model is to minimize the execution time of all workflow instances. Then, a hybrid scheduling method based on the batch strategy and an improved genetic algorithm termed fragmentation based genetic algorithm is proposed according to the characters of instance-intensive cloud workflow, where task priority dispatching rules are also taken into account. Simulations are conducted to compare the proposed method with the canonical genetic algorithm and two heuristic algorithms. Our simulation results demonstrate that the proposed method can considerably enhance the search efficiency of the genetic algorithm and is able to considerably outperform the compared algorithms, in particular when the number of workflow instances is high and the computational resource available for optimization is limited.

We show that some natural output conventions for error-free computation in chemical reaction networks (CRN) lead to a common level of computational expressivity. Our main results are that the standard consensus-based output convention have equivalent computational power to (1) existence-based and (2) democracy-based output conventions. The CRNs using the former output convention have only “yes” voters, with the interpretation that the CRN’s output is yes if any voters are present and no otherwise. The CRNs using the latter output convention define output by majority vote among “yes” and “no” voters. Both results are proven via a generalized framework that simultaneously captures several definitions, directly inspired by a Petri net result of Esparza, Ganty, Leroux, and Majumder [CONCUR 2015]. These results support the thesis that the computational expressivity of error-free CRNs is intrinsic, not sensitive to arbitrary definitional choices.