Epistocracy Algorithm: A Novel Hyper-heuristic Optimization Strategy for Solving Complex Optimization Problems
Seyed Ziae Mousavi Mojab, Seyedmohammad Shams, Hamid Soltanian-Zadeh,, Farshad Fotouhi

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…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Advanced Multi-Objective Optimization Algorithms
