Epistocracy Algorithm: A Novel Hyper-heuristic Optimization Strategy for Solving Complex Optimization Problems

TL;DR

The paper introduces Epistocracy, a novel hyper-heuristic evolutionary algorithm inspired by socio-political concepts, designed to effectively solve complex optimization problems through adaptive multi-population strategies and advanced sampling techniques.

Contribution

It presents a new evolutionary algorithm that incorporates socio-political behavior, adaptive leadership, and stratified sampling to enhance optimization performance and avoid local optima.

Findings

Outperforms state-of-the-art algorithms on benchmark functions

Achieves higher accuracy and robustness on MNIST dataset

Demonstrates improved convergence and exploration capabilities

Abstract

This paper proposes a novel evolutionary algorithm called Epistocracy which incorporates human socio-political behavior and intelligence to solve complex optimization problems. The inspiration of the Epistocracy algorithm originates from a political regime where educated people have more voting power than the uneducated or less educated. The algorithm is a self-adaptive, and multi-population optimizer in which the evolution process takes place in parallel for many populations led by a council of leaders. To avoid stagnation in poor local optima and to prevent a premature convergence, the algorithm employs multiple mechanisms such as dynamic and adaptive leadership based on gravitational force, dynamic population allocation and diversification, variance-based step-size determination, and regression-based leadership adjustment. The algorithm uses a stratified sampling method called Latin Hypercube Sampling (LHS) to distribute the initial population more evenly for exploration of the search space and exploitation of the accumulated knowledge. To investigate the performance and evaluate the reliability of the algorithm, we have used a set of multimodal benchmark functions, and then applied the algorithm to the MNIST dataset to further verify the accuracy, scalability, and robustness of the algorithm. Experimental results show that the Epistocracy algorithm outperforms the tested state-of-the-art evolutionary and swarm intelligence algorithms in terms of performance, precision, and convergence.