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Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport

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Abstract

In this paper a new metaheuristic called the Most Valuable Player Algorithm (MVPA) is proposed for solving optimization problems. The developed algorithm is inspired from sport where players form teams, then these players compete collectively (in teams) in order to win the championship and they compete also individually in order to win the MVP trophy. The performances of MVPA are evaluated on a set of 100 mathematical test functions. The obtained results are compared with the ones obtained using 13 well-known optimization algorithms. These results demonstrate that, the MVPA is a very competitive optimization algorithm, it converges rapidly (with smaller number of functions evaluations) and more successfully (with higher overall success percentage) than the compared algorithms. Therefore, further developments and applications of MVPA would be worth investigating in future studies.

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Abbreviations

ABC:

Artificial Bee Colony

BH:

Black Hole

BMO:

Bird Mating Optimizer

CBO:

Colliding Bodies Optimization

DE:

Differential Evolution

DSA:

Differential Search Algorithm

E:

Experiment

EM:

Electromagnetism-like mechanism

FA:

Firefly Algorithm

fitness:

Fitness (strength or efficiency or rating) of a team

fitnessN:

Normalized fitness

FranchisePlayer:

Franchise player of a team

\( f\left( {\mathbf{x}} \right) \) :

Objective function

\( g_{i} \left( {\mathbf{x}} \right) \) :

Set of equality constraints

GA:

Genetic Algorithm

GSA:

Gravitational Search Algorithm

\( h_{j} \left( {\mathbf{x}} \right) \) :

Set of inequality constraints

HS:

Harmony Search

LCA:

League Championship Algorithm

MaxNFix:

Maximum number of fixtures

MVP:

Most valuable player

MVPA:

Most Valuable Player Algorithm

nP:

Number of players of one team

nTi :

Number of teams with the same number of players

ObjFunction:

The name of the objective function

Playeri :

A player in the population

PlayersSize:

Number of players in the league (population size)

Pr:

Probability

ProblemSize:

Problem dimension

PSO:

Particle Swarm Optimization

S1,1 :

Skill

SA:

Simulated Annealing

TEAMi :

Groupe of players

TeamsSize:

Number of teams in the league

TLBO:

Teaching–Learning-Based Optimization

\( x_{k}^{min} \;{\text{and}}\;x_{k}^{max} \) :

Domain constraints

\( {\mathbf{x}} = \left\{ {x_{1} ,x_{2} , \ldots ,x_{n} } \right\} \) :

Vector of design variables

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Authors and Affiliations

  1. Laboratory of Electrical Engineering of Constantine, LGEC, Department of Electrical Engineering, University of Freres Mentouri Constantine, 25000, Constantine, Algeria

    H. R. E. H. Bouchekara

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  1. H. R. E. H. Bouchekara
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Correspondence to H. R. E. H. Bouchekara.

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Bouchekara, H.R.E.H. Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport. Oper Res Int J 20, 139–195 (2020). https://doi.org/10.1007/s12351-017-0320-y

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