state transition rules
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2021 ◽  
pp. 002029402110642
Author(s):  
Dongping Qiao ◽  
Yajing Wang ◽  
Jie Pei ◽  
Wentong Bai ◽  
Xiaoyu Wen

This paper studies the green single-machine scheduling problem that considers the delay cost and the energy consumption of manufacturing equipment and builds its integrated optimization model. The improved ant colony scheduling algorithm based on the Pareto solution set is used to solve this problem. By setting the heuristic information, state transition rules, and other core parameters reasonably, the performance of the algorithm is improved effectively. Finally, the model and the improved algorithm are verified by the simulation experiment of 10 benchmark cases.


2020 ◽  
Vol 39 (4) ◽  
pp. 5329-5338
Author(s):  
Yan Zheng ◽  
Qiang Luo ◽  
Haibao Wang ◽  
Changhong Wang ◽  
Xin Chen

The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms’ simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.


2019 ◽  
Vol 6 (1) ◽  
pp. 181198 ◽  
Author(s):  
Andrew Adamatzky

We simulate an actin filament as an automaton network. Every atom takes two or three states and updates its state, in discrete time, depending on a ratio of its neighbours in some selected state. All atoms/automata simultaneously update their states by the same rule. Two state transition rules are considered. In semi-totalistic Game of Life like actin filament automaton atoms take binary states ‘0’ and ‘1’ and update their states depending on a ratio of neighbours in the state ‘1’. In excitable actin filament automaton atoms take three states: resting, excited and refractory. A resting atom excites if a ratio of its excited neighbours belong to some specified interval; transitions from excited state to refractory state and from refractory state to resting state are unconditional. In computational experiments, we implement mappings of an 8-bit input string to an 8-bit output string via dynamics of perturbation/excitation on actin filament automata. We assign eight domains in an actin filament as I/O ports. To write True to a port, we perturb/excite a certain percentage of the nodes in the domain corresponding to the port. We read outputs at the ports after some time interval. A port is considered to be in a state True if a number of excited nodes in the port's domain exceed a certain threshold. A range of eight-argument Boolean functions is uncovered in a series of computational trials when all possible configurations of eight-elements binary strings were mapped onto excitation outputs of the I/O domains.


2015 ◽  
Vol 25 (02) ◽  
pp. 1550030 ◽  
Author(s):  
Andrew Adamatzky ◽  
Richard Mayne

Actin is a globular protein which forms long filaments in the eukaryotic cytoskeleton, whose roles in cell function include structural support, contractile activity to intracellular signaling. We model actin filaments as two chains of one-dimensional binary-state semi-totalistic automaton arrays to describe hypothetical signaling events therein. Each node of the actin automaton takes state "0" (resting) or "1" (excited) and updates its state in discrete time depending on its neighbor's states. We analyze the complete rule space of actin automata using integral characteristics of space-time configurations generated by these rules and compute state transition rules that support traveling and mobile localizations. Approaches towards selection of the localization supporting rules using the global characteristics are outlined. We find that some properties of actin automata rules may be predicted using Shannon entropy, activity and incoherence of excitation between the polymer chains. We also show that it is possible to infer whether a given rule supports traveling or stationary localizations by looking at ratios of excited neighbors that are essential for generations of the localizations. We conclude by applying biomolecular hypotheses to this model and discuss the significance of our findings in context with cell signaling and emergent behavior in cellular computation.


2012 ◽  
Vol 616-618 ◽  
pp. 2091-2096 ◽  
Author(s):  
Hong Hong ◽  
Fang Liu

This article proposed an Adaptive Binary Ant Colony Optimization Algorithm, which is based on the dual network diagram, designed to state transition rules and information update rules, and then according to the algorithm processes adjust information volatilizing factor dynamically, Verify the validity and superiority of the algorithm.


2012 ◽  
Vol 22 (02) ◽  
pp. 1250023 ◽  
Author(s):  
GENARO J. MARTÍNEZ ◽  
ANDREW ADAMATZKY ◽  
RAMON ALONSO-SANZ

We show techniques of analyzing complex dynamics of cellular automata (CA) with chaotic behavior. CA are well-known computational substrates for studying emergent collective behavior, complexity, randomness and interaction between order and chaotic systems. A number of attempts have been made to classify CA functions on their space-time dynamics and to predict the behavior of any given function. Examples include mechanical computation, λ and Z-parameters, mean field theory, differential equations and number conserving features. We aim to classify CA based on their behavior when they act in a historical mode, i.e. as CA with memory. We demonstrate that cell-state transition rules enriched with memory quickly transform a chaotic system converging to a complex global behavior from almost any initial condition. Thus, just in few steps we can select chaotic rules without exhaustive computational experiments or recurring to additional parameters. We provide an analysis of well-known chaotic functions in one-dimensional CA, and decompose dynamics of the automata using majority memory exploring glider dynamics and reactions.


2010 ◽  
Vol 22 (5) ◽  
pp. 669-676 ◽  
Author(s):  
Takeshi Ishida ◽  

Clarifying generalized self-reproduction is basic to applications in fields such as molecular machine production in nanotechnology and synthetic biology. The two-dimensional cellular automaton model we developed simulated cellular self-reproduction using a few state transition rules.


2006 ◽  
Vol 16 (04) ◽  
pp. 1011-1018 ◽  
Author(s):  
KOHJI TOMITA ◽  
HARUHISA KUROKAWA ◽  
SATOSHI MURATA

Graph automata define symbol dynamics of graph structures, which are capable of generating structures in addition to describing state transition. Rules of the graph automata are uniform and are therefore suitable for evolutionary computation. This paper shows that various self-replications are possible in graph automata, and that evolutionary computation is applicable to automatic rule generation to obtain self-replicating patterns of graph automata.


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