Optimization of Demand and Supply Equilibrium equation by using Salp Swarm Algorithm

Ouaar Fatima
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Abstract

The operation of demand and supply in a market is known as the market mechanism. The market will be in equilibrium at price (P), when quantity (Q) will be bought and sold. In equilibrium the quantity of a good supplied by producers equals the quantity demanded by consumers. Due to the fact that traditional deterministic methods or algorithms do not cope well to solve a large number of problems in practice; The aim of this paper is to solve approximately the demand and supply equilibrium equation which has an imperative role to describe the relation between consumers/ producers and price/quantity by means of the Salp Swarm Algorithm (SSA), inspired by the swarming behavior of salps when searching foods in deep oceans as well as the Genetic Algorithm (GA) inspired by the process of natural selection. The demand and supply equilibrium equation as an Initial Value Problem (IVP) is considered as an optimization problem, since it can almost be solved by classical mathematical tools with less precision. The effectiveness of the proposed method is tested via a simulation study between the exact results, the SSA and GA results. The comparison between these performances after many replications shows that SSA is a very powerful and can produce robust solutions on low dimensional problems with minimal error.
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基于Salp群算法的供需平衡方程优化
市场中需求和供给的运行被称为市场机制。当数量(Q)被买卖时,市场在价格(P)处处于均衡状态。在均衡状态下,生产者提供的某种商品的数量等于消费者的需求量。由于传统的确定性方法或算法不能很好地解决实际中的大量问题;本文的目的是利用受深海中Salp寻找食物的群体行为启发的Salp Swarm算法(SSA)和受自然选择过程启发的遗传算法(GA),近似求解对描述消费者/生产者和价格/数量关系有重要作用的供需平衡方程。需求和供给平衡方程作为一个初值问题(IVP)被认为是一个优化问题,因为它几乎可以用经典的数学工具来求解,但精度较低。通过对精确结果、SSA和GA结果的仿真研究,验证了该方法的有效性。在多次重复之后,对这些性能的比较表明,SSA是一种非常强大的方法,可以以最小的误差产生低维问题的鲁棒解。
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