Nature-Inspired Algorithms for Competitive Advantage
We encounter algorithms all around us in most mundane situations every day. They are indeed quite unobtrusive and quietly at work most of the time, but their impact on our lives is sizeable and certain.
For instance, when we see a container ship carrying a well-balanced load, we can be sure that an algorithm has ensured its appropriate distribution and optimal balance. Paper manufacturers also use algorithms to minimize wastage in their factories. The local taxi service definitely leverages many algorithms to know your preferences and ensure your ride is the shortest and the most comfortable. Whether you believe it or not, when you encounter a crate of apples in the local market, you can bet that an algorithm was used to determine the number of apples that a crate should carry!
Certain pockets of science go as far as saying that all organisms are just bio-algorithms and life is just data processing though it is debatable if human consciousness can ever be cloned however intelligent algorithms become (“Homo Deus: A Brief History of Tomorrow “ by Yuval Noah Harari).
Why Nature-Inspired Algorithms (NIA)?
While algorithms are constantly refined and honed by dint of years of hard work and seem to demonstrate an assurance that they indeed work for most, if not all business scenarios, they often prove inefficient and practically ineffective. They either don’t find global or near-global optimum or demand too many resources to find one. They don’t measure up in either case.
The reason for this is that the current business problems have an enormous solution search space, and it proves daunting and well-nigh impossible for algorithms to cover this vast search space quickly and efficiently without wasted cycles or requiring inordinately high resources.
Here is where natural processes often come up as an advantage for their uncanny efficiency and unmistakable elegance in doing similar activities, thanks to years of evolution and perfection. For example, a spider can weave a web with remarkable precision, strength, and elasticity and practically no heat! A cuckoo bird, for example, has perfected the art of locating nests for its offspring by poaching pre-built nests, and ants locate the best path from their anthills to the food source in a matter of minutes.
Hence, there has been a significant focus on studying such nature-inspired algorithms at work behind natural processes around us and newer algorithms are being created by abstracting these natural processes.
What are Some of the Most Common NIA?
The following sections highlight some of the commonly used and awe-inspiring nature-inspired algorithms:
Ant Colony Optimization (ACO)
In nature, ants seem to wander randomly, and upon finding food, return to their colony while laying down pheromone trails along the way. If other ants find such a path, they are likely not to keep traveling at random, but instead to follow the trail, and reinforcing it with more pheromone. The longer it takes an ant to travel up and down the path, the longer the pheromones have to evaporate. A short path, by comparison, gets marched over more frequently, and thus the pheromone density becomes higher quickly on shorter paths than on longer ones. Figure below details the pseudocode for the ACO from Xin-She Yang’s works.
Figure 1 Pseudocode for the ACO
Genetic Algorithm (GA)
The process of evolution with its dominant mantra being the survival of the fittest provides many abstractions to search for solutions. Newer generations are arrived at by gene single and dual crossover, gene mutations, and other such opportunistic and intelligent methods.
The following figure demonstrates the use of GA to improve upon the solution to maximize a function. As research indicates, GA has been instrumental in resolving Flexible Job Shop Scheduling Problems (FJSSP) and in various types of engineering design problems and database search methods.
NIA Applications are Becoming More Ubiquitous
Nature-inspired algorithms have proved to be extremely successful in diverse business applications. Problems that can be visualized as a series of network graphs are best solved by using ACO, while Genetic Algorithms have proved effective in the area of scheduling optimization problems. Similarly, problems which are associated with large search spaces are easily addressed using the Cuckoo Search (CS) algorithms. Swarm intelligence using the bat and the firefly abstractions have also proved very fruitful.
Some business problems that can be solved using NIA relate to cargo operations on the airside, routing problems in fleet management, and portfolio optimization in wealth management contexts. In the case of agricultural operations, NIA used in the area of feed optimization and layout design of automated farms have been known to yield better return than before.
Nature-inspired algorithms no longer belong to just the realm of research; they are very much a part of business tool kit. As world around is becoming increasingly digital and businesses are depending on instantaneous solutions for large and dynamic problems, nature-inspired algorithms are making a critical difference to the business world.