Introducing Ulimisana Optimization Algorithm Based on Ubuntu Philosophy

Tshifhiwa Maumela,Tshilidzi Marwala,Fulufhelo Nelwamondo

doi:10.1109/access.2020.3026821

Tshifhiwa Maumela, Tshilidzi Marwala + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3026821

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 28	License type: CC BY 4.0

Affiliation: University of Johannesburg

Abstract

In this paper we give introduction to the concepts of Ubuntu and how we used Mechanism design concepts to construct Ubuntu as an optimisation algorithm. Ubuntu philosophy is old and consists of many oral proverbs that have been documented in recent years. This work thus introduces an incentive mechanism based on Ubuntu, thus called Ubuntu Incentive, which is modelled according to Mechanism Design principles. This incentive scheme is introduced as a fitness function which the algorithm tries to improve. To achieve this, the algorithm draws inspiration from Bantu proverbs that guide how individuals ought to behave within the Ubuntu community. Trust is an important element within these communities and it is shown how trust influences the obtaining of the Pareto efficiency. The algorithm is introduced with different mathematical configurations which are tested against each other. Ulimisana optimisation algorithm (UOA) manages to solve the benchmark test functions used in this work. This is found to be in accordance to the Ubuntu philosophy as used in the Ulimisana/Letsema practice amongst the Bantu people of Southern Africa. The UOA performed better in some benchmark test function when compared to other algorithms and coming second on most performance to PSO for most test benchmark function.

Highlights

In recent years there has been a growth in number of meta-heuristic optimisation algorithms inspired by nature where they can be divided into the evolutionary algorithms and swarm intelligence algorithms
Swarm intelligence are inspired by how animals, plants organise themselves to achieve different tasks mostly to look for food, these includes Ant Colony optimization (ACO) [11]–[13], Particle Swarm Optimization (PSO) [14], Social Spider Algorithm (SSA) [15], Grey Wolf Algorithm (GWF) [16], Moth Flame Algorithm (MFA) [17], Firefly Algorithm (FA) [18], Artificial Bee Colony (ABC) [19], Chaotic Bat Algorithm (CBA) [20], Harris hawks optimization (HHO) [21], Barnacles Mating Optimizer (BMO) [22], Salp Swarm Algorithm(SSA) [23]
Been developed which were inspired by physical laws, these include Big Bang–Big Crunch(BB-BC) [24], Central Force optimization (CFO) [25], Gravitational Search Algorithm (GSA) [26], Fireworks Algorithm(FA) [27], Simulated Annealing (SA) [28], [29] and more detailed ones are listed by Salcedo-Sanz [30]

Summary

INTRODUCTION

In recent years there has been a growth in number of meta-heuristic optimisation algorithms inspired by nature where they can be divided into the evolutionary algorithms and swarm intelligence algorithms. In this paper we introduce a new meta-heuristic algorithm inspired by Ubuntu philosophy which was devised thousands of years ago and of which the African people in Southern Africa used [38] This algorithm considers the fact that the agents can have their own motives despite the incentive mechanism and considers the nature of agents diverting from a communal benefit and bettering their own payoffs. Self-interested agents always act in selfish ways which in many cases harm the benefits that the whole community can benefit from This is evident in the classic prisoners dilemma where the agents best rational responses is to chose a solution that leaves her worse off than if she and her counterpart had to choose the solution that benefit all of them they would end up with a strategy that leaves them better off than in a selfish state.

INCENTIVE MECHANISM DESIGN AND UBUNTU PAYOFFS

POSITION UPDATE

RESULTS

CONCLUSION