A Cooperative Group Optimization System
Xiao-Feng Xie, Jiming Liu, Zun-Jing Wang

TL;DR
The paper introduces a cooperative group optimization system that integrates multiple heterogeneous search heuristics within a flexible, script-based framework, enabling efficient constrained optimization with competitive performance.
Contribution
It presents a novel, modular CGO system combining cooperative group principles with low-level algorithm portfolios, facilitating easy customization and evolution of optimization strategies.
Findings
Achieves competitive results on benchmark constrained optimization problems.
Provides a flexible, script-based framework for defining and evolving optimization algorithms.
Demonstrates effective cooperation among heterogeneous search heuristics.
Abstract
A cooperative group optimization (CGO) system is presented to implement CGO cases by integrating the advantages of the cooperative group and low-level algorithm portfolio design. Following the nature-inspired paradigm of a cooperative group, the agents not only explore in a parallel way with their individual memory, but also cooperate with their peers through the group memory. Each agent holds a portfolio of (heterogeneous) embedded search heuristics (ESHs), in which each ESH can drive the group into a stand-alone CGO case, and hybrid CGO cases in an algorithmic space can be defined by low-level cooperative search among a portfolio of ESHs through customized memory sharing. The optimization process might also be facilitated by a passive group leader through encoding knowledge in the search landscape. Based on a concrete framework, CGO cases are defined by a script assembling over…
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