Abstract
Finding optimal solutions to NP-Hard problems requires exponential time with respect to the size of the problem. Consequently, heuristic methods are usually utilized to obtain approximate solutions to problems of such difficulty. In this paper, a novel swarm-based nature-inspired metaheuristic algorithm for optimization is proposed. Inspired by human collective intelligence, Wisdom of Artificial Crowds (WoAC) algorithm relies on a group of simulated intelligent agents to arrive at independent solutions aggregated to produce a solution which in many cases is superior to individual solutions of all participating agents. We illustrate superior performance of WoAC by comparing it against another bio-inspired approach, the Genetic Algorithm, on one of the classical NP-Hard problems, the Travelling Salesperson Problem. On average a 3% - 10% improvement in quality of solutions is observed with little computational overhead.
Highlights
A large number of important problems have been shown to be NP-Hard [1]
Proportion of coincidence of path with other subjects was calculated by counting the number of edges a solution has in common with other solutions, and dividing the number of agreeing edges by the number of cities to get the percentage of agreement
On average a 3% - 10% improvement could be seen in cases where Wisdom of Artificial Crowds (WoAC) had improvement over standalone Genetic Algorithms (GA)
Summary
A large number of important problems have been shown to be NP-Hard [1]. Problems in that computational class are believed to require exponential time, in the worst case, to be solved. Most metaheuristic algorithms in optimization and search have been modeled on processes observed in biological systems [3,4,5]: Genetic Algorithms (GA) [6], Genetic Programming (GP) [7], Cellular Automata (CA) [8], Artificial Neural Networks (ANN), Artificial Immune System (AIS) [9], or in the surrounding environment: Intelligent Water Drops (IWD) [10], Gravitational Search Algorithm (GSA) [11], Stochastic Diffusion Search (SDS) [12], River Formation Dynamics (RFD) [2], Electromagnetism-Like Mechanism (EM) [13], Particle Swarm Optimization (PSO) [14], Charged System Search (CSS) [15], Big Bang-Big Crunch (BB-BC) [16] Continuing this trend of nature-inspired solutions a large number of animal or plant behavior-based algorithms have been proposed in recent years: Ant Colony Optimization (ACO) [17], Bee Colony Optimization (BCO) [18], Bacterial Foraging Optimization (BFO) [19], Glowworm Swarm Optimization (GSO) [20], Firefly Algorithm (FA) [21], Cuckoo Search (CS) [22], Flocking Birds (FB) [23], Harmony Search (HS) [24], Monkey Search (MS) [25] and Invasive Weed Optimization (IWO) [26].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Intelligent Learning Systems and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
