Nature Inspired Optimisation Tools Assignment Help

Computing in Computer Integrated Manufacturing - Nature Inspired Optimisation Tools

Nature Inspired Optimisation Tools


As we know that complex combinatorial optimization problems happen in almost each field as like economy, engineering or commerce, industry. These problems are categorized as complex since there is no algorithm known which resolves them in polynomial time. Problems exhibiting in nature are termed as NP-hard. Though, efforts have been completed to resolve these problems in a vast number of practical settings and thus, a large number of algorithmic approaches are proposed to tackle them. The existing methods can approximately be classified into exact and rough algorithms. Accurate algorithms try to determine an optimal solution and show that the solution obtained is in reality an optimal one. This category involves algorithms as like: Bound and Branch, Dynamic backtracking, programming, as accurate algorithms show poor performance for many problems, some types of rough algorithms that offer high quality solutions to combinatorial problems in short computational time are planned.

Heuristics based upon Nature or Bio-inspired algorithms are an approximate algorithm that has the capability to identify an optimal or near-optimal solution in minimum computational time. One commonality such exists in between all such Bio-inspired algorithms is such they have been evolved from several natural processes and mimic their behaviour so as to raise their effectiveness. The family of Bio-inspired algorithms involves genetic algorithm, simulated annealing. Details the family of Bio-inspired algorithms and their analogical behaviour are described in Table no.1.



A set of chromosome evolve by means of mutations and crossover.




Exploits an analogy between the way in which a metal cools and freezes into minimum energy crystalline structure i.e. the annealing procedure and then search for a minimum in a more general system.

Ant Colony


It resembles an analogy with the foraging behaviour of ants, especially their way of searching the food from the nest.

Particle Swarm Optimisation


Based on swarm intelligence and gains its broad guideline from the socio-cognitive perspectives.

Application Of Particle Swarm Optimisation Algorithm In Reliability Optimisation Problem BASICS OF ACO Or Ant Colony Optimization
Basics Of Particle Swarm Oprimisation How to Solve a Combinatorial Optimization Problem by using ACO
Mathematical Formulation Of Ant Colony Optimization Algorithm Objectives
Prevention of Very Quick Convergence of the Algorithm Problems that can be resolved by using ACO
Stagnation Avoidance Time-Varying Acceleration Coefficients or TVAC
Variants Of PSO Algorithm

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