Optimisation of a Large, Constrained Simulation Model using Compressed Annealing
研究了压缩退火算法在大型、受约束的仿真模型中寻找盈利方案的效果，以澳大利亚小麦带引入法国蛇麻草为例，发现该算法比标准模拟退火和遗传算法更可靠地处理约束，且法国蛇麻草在高杂草或高羊价时是经济轮作选择。
Abstract. Simulation models are valuable tools in the analysis of complex, highly constrained economic systems unsuitable for solution by mathematical programming. However, model size may hamper the efforts of practitioners to identify efficiently the most valuable management strategy. This paper investigates the efficacy of a new stochastic search procedure, compressed annealing, for the identification of profitable solutions in large, constrained systems. The algorithm is used to examine the value of incorporating a sown annual pasture, French serradella ( Ornithopus sativus Brot. cv. Cadiz ), between extended cropping sequences in the central wheatbelt of Western Australia. Compressed annealing is shown to be a reliable means of considering constraints in complex optimisation problems relative to the incorporation of fixed penalty factors in standard simulated annealing and genetic algorithms. French serradella is found to be an economic break pasture in the study region when weed populations are high or sheep production is lucrative.