# Multiple Global Peaks Big Bang-Big Crunch Algorithm for Multimodal Optimization

arXiv: https://arxiv.org/abs/2410.18102 · HTML: https://mlsanity.com/paper/2410.18102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 56 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Benchmarking HillVallEA for the GECCO 2019 Competition on Multimodal Optimization](https://mlsanity.com/paper/1907.10988/) (2019)
- [Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism](https://mlsanity.com/paper/2205.10052/) (2022)
- [Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering](https://mlsanity.com/paper/1810.07085/) (2018)
- [Design Optimizer for Planar Soft-Growing Robot Manipulators](https://mlsanity.com/paper/2310.03374/) (2023)
- [Multimodal Optimization with k-Cluster Big Bang-Big Crunch Algorithm and Postprocessing Methods for Identification and Quantification of Optima](https://mlsanity.com/paper/2401.06153/) (2023)
- [The Impact of Evolutionary Computation on Robotic Design: A Case Study with an Underactuated Hand Exoskeleton](https://mlsanity.com/paper/2403.15812/) (2024)

## Abstract (arXiv)

The main challenge of multimodal optimization problems is identifying multiple peaks with high accuracy in multidimensional search spaces with irregular landscapes. This work proposes the Multiple Global Peaks Big Bang-Big Crunch (MGP-BBBC) algorithm, which addresses the challenge of multimodal optimization problems by introducing a specialized mechanism for each operator. The algorithm expands the Big Bang-Big Crunch algorithm, a state-of-the-art metaheuristic inspired by the universe's evolution. Specifically, MGP-BBBC groups the best individuals of the population into cluster-based centers of mass and then expands them with a progressively lower disturbance to guarantee convergence. During this process, it (i) applies a distance-based filtering to remove unnecessary elites such that the ones on smaller peaks are not lost, (ii) promotes isolated individuals based on their niche count after clustering, and (iii) balances exploration and exploitation during offspring generation to target specific accuracy levels. Experimental results on twenty multimodal benchmark test functions show that MGP-BBBC generally performs better or competitively with respect to other state-of-the-art multimodal optimizers.