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City councils evolution: a socio-inspired metaheuristic optimization algorithm

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Abstract

The supreme council of a city is usually formed by the evolution of councils from the smallest neighborhoods to the largest ones, regions, and finally the whole city. Council members of a region try to improve their performance to be selected as the boss of the council in the future election, and also a member of the larger region council. This fact motivates us to propose a socio-inspired metaheuristic optimization algorithm [named as city councils evolution (CCE)] inspired by the evolution of city councils. To analyze the effectiveness of CCE, it is applied to solve 20 general test functions and 29 benchmark functions from CEC 2017. Results of CCE are compared with the effectiveness of nine popular and new optimization algorithms belonging to different classes: SHADE and LSHADE-cnEpSin as optimization algorithms with high performance and winners of IEEE CEC competitions (2013 and 2017), and EO, BWO, PO, BMO, CHOA, AO, and WHO as newly developed algorithms (2020 and 2021). According to the average rank of Friedman test, for all 49 test functions, CCE outperforms EO, BWO, PO, BMO, CHOA, AO, and WHO by 65%, 95%, 64%, 68%, 80%, 74%, and 71%, respectively, whereas it is outperformed by SHADE and LSHADE-cnEpSin by 49% and 65%, respectively. Finally, the obtained results of solving real-world constrained optimization problems by the proposed algorithm show that it has better performance compared to some good algorithms in the literature. The source code of the CCE algorithm is publicly available at https://github.com/EinPira/City-Councils-Evolution-Algorithm.

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Abbreviations

CCE:

City councils evolution

SHADE:

Success-history based adaptive DE

LSHADE-cnEpSin:

Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood

EO:

Equilibrium optimizer

BWO:

Black widow optimization algorithm

PO:

Political optimizer

BMO:

Barnacles mating optimizer

CHOA:

Chimp optimization algorithm

AO:

Aquila optimizer

WHO:

Wild horse optimizer

RMSE:

Root-mean-square error

ILS:

Iterated local search

SAA:

Simulated annealing algorithm

VSA:

Vortex search algorithm

TS:

Tabu search

GA:

Genetic algorithm

PSO:

Particle swarm optimization

ACO:

Ant colony optimization

MSSA:

Multi-objective salp swarm algorithm

MOEPO:

Multi-objective emperor penguin optimizer

MOGSABAT:

Multi-objective gravitational search algorithm and BAT algorithm

GP:

Genetic programming

DE:

Differential evolution

MA:

Memetic algorithm

BHA:

Black hole algorithm

GBO:

Gradient-based optimizer

TLBO:

Teaching–learning-based optimization

POA:

Parliamentary optimization algorithm

GPO:

Greedy politics optimization

ECO:

Election campaign optimization algorithm

TEO:

Thermal exchange optimization

WEO:

Water evaporation optimization

LSO:

Lightning search algorithm

OIO:

Optics inspired optimization

ECO:

Election campaign optimization algorithm

CS:

Cuckoo search

DE:

Dolphin echolocation

WOA:

Whale optimization algorithm

GWO:

Grey wolf optimizer

SSA:

Salp swarm algorithm

BA:

Bat algorithm

MBO:

Migrating birds optimization

GSO:

Group search optimizer

MFPA:

Modified flower pollination algorithm

HOA:

Horse herd optimization algorithm

GSA:

Gravitational search algorithm

BBC:

Big-bang crunch

GIO:

Gravitational interaction optimization

ANOVA:

One way analysis of variance

LGSI:

Ludo game-based swarm intelligence

MFO:

Moth flame optimization

GOA:

Grass-hopper optimization algorithm

SCA:

Sine cosine algorithm

GWO:

Gray wolf optimization

PRO:

Poor and rich optimization

SGO:

Social group optimization

PIC:

A performance improvement criterion

SN:

Smallest neighborhood

CT:

Councils tree

D:

A dimension of a given test function

N :

The population size

C :

An array with size N to implement the councils tree

h :

The height of the councils tree

crN :

The number of performance improvement criteria

max :

A function that returns the input with the highest fitness

α :

A random value and coefficients of two current solutions in the applied formula in the improve function

fit :

A fitness of an individual

d :

Number of council members

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Acknowledgements

We would like to thank Dr. Afshin Faramarzi and Dr. Ali Sadollah for providing us the Matlab codes of CEC 2017 test functions and the constrained version of the water cycle algorithm.

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  1. Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, 5375171379, Iran

    Einollah Pira

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Pira, E. City councils evolution: a socio-inspired metaheuristic optimization algorithm. J Ambient Intell Human Comput 14, 12207–12256 (2023). https://doi.org/10.1007/s12652-022-03765-5

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