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Contributions to the development of the CRO-SL algorithm: Engineering applications problems

Carlos Camacho-Gómez

TL;DR

This work introduces the Coral Reef Optimization with Substrate Layer (CRO-SL), a parallel, competitive co-evolution framework that integrates multiple exploration strategies as substrate layers to solve hard engineering optimization problems. CRO-SL is applied to four engineering domains—battery scheduling in Micro-Grids, Tuned Mass Damper design and placement, active vibration control with inertial-m mass actuators, and textile planar RFID antennas—demonstrating robust performance and enabling comparative analysis of search procedures. Across problems, CRO-SL achieves significant improvements over baseline or single-substrate approaches and reveals which substrates contribute most to different tasks, providing a practical means to benchmark exploration methods. The results highlight CRO-SL’s versatility, computational efficiency, and potential to guide meta-heuristic selection and design in real-world engineering contexts.

Abstract

This Ph.D. thesis discusses advanced design issues of the evolutionary-based algorithm \textit{"Coral Reef Optimization"}, in its Substrate-Layer (CRO-SL) version, for optimization problems in Engineering Applications. The problems that can be tackled with meta-heuristic approaches is very wide and varied, and it is not exclusive of engineering. However we focus the Thesis on it area, one of the most prominent in our time. One of the proposed application is battery scheduling problem in Micro-Grids (MGs). Specifically, we consider an MG that includes renewable distributed generation and different loads, defined by its power profiles, and is equipped with an energy storage device (battery) to address its programming (duration of loading / discharging and occurrence) in a real scenario with variable electricity prices. Also, we discuss a problem of vibration cancellation over structures of two and four floors, using Tuned Mass Dampers (TMD's). The optimization algorithm will try to find the best solution by obtaining three physical parameters and the TMD location. As another related application, CRO-SL is used to design Multi-Input-Multi-Output Active Vibration Control (MIMO-AVC) via inertial-mass actuators, for structures subjected to human induced vibration. In this problem, we will optimize the location of each actuator and tune control gains. Finally, we tackle the optimization of a textile modified meander-line Inverted-F Antenna (IFA) with variable width and spacing meander, for RFID systems. Specifically, the CRO-SL is used to obtain an optimal antenna design, with a good bandwidth and radiation pattern, ideal for RFID readers. Radio Frequency Identification (RFID) has become one of the most numerous manufactured devices worldwide due to a reliable and inexpensive means of locating people. They are used in access and money cards and product labels and many other applications.

Contributions to the development of the CRO-SL algorithm: Engineering applications problems

TL;DR

This work introduces the Coral Reef Optimization with Substrate Layer (CRO-SL), a parallel, competitive co-evolution framework that integrates multiple exploration strategies as substrate layers to solve hard engineering optimization problems. CRO-SL is applied to four engineering domains—battery scheduling in Micro-Grids, Tuned Mass Damper design and placement, active vibration control with inertial-m mass actuators, and textile planar RFID antennas—demonstrating robust performance and enabling comparative analysis of search procedures. Across problems, CRO-SL achieves significant improvements over baseline or single-substrate approaches and reveals which substrates contribute most to different tasks, providing a practical means to benchmark exploration methods. The results highlight CRO-SL’s versatility, computational efficiency, and potential to guide meta-heuristic selection and design in real-world engineering contexts.

Abstract

This Ph.D. thesis discusses advanced design issues of the evolutionary-based algorithm \textit{"Coral Reef Optimization"}, in its Substrate-Layer (CRO-SL) version, for optimization problems in Engineering Applications. The problems that can be tackled with meta-heuristic approaches is very wide and varied, and it is not exclusive of engineering. However we focus the Thesis on it area, one of the most prominent in our time. One of the proposed application is battery scheduling problem in Micro-Grids (MGs). Specifically, we consider an MG that includes renewable distributed generation and different loads, defined by its power profiles, and is equipped with an energy storage device (battery) to address its programming (duration of loading / discharging and occurrence) in a real scenario with variable electricity prices. Also, we discuss a problem of vibration cancellation over structures of two and four floors, using Tuned Mass Dampers (TMD's). The optimization algorithm will try to find the best solution by obtaining three physical parameters and the TMD location. As another related application, CRO-SL is used to design Multi-Input-Multi-Output Active Vibration Control (MIMO-AVC) via inertial-mass actuators, for structures subjected to human induced vibration. In this problem, we will optimize the location of each actuator and tune control gains. Finally, we tackle the optimization of a textile modified meander-line Inverted-F Antenna (IFA) with variable width and spacing meander, for RFID systems. Specifically, the CRO-SL is used to obtain an optimal antenna design, with a good bandwidth and radiation pattern, ideal for RFID readers. Radio Frequency Identification (RFID) has become one of the most numerous manufactured devices worldwide due to a reliable and inexpensive means of locating people. They are used in access and money cards and product labels and many other applications.

Paper Structure

This paper contains 50 sections, 38 equations, 48 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction and algorithmics state of the art
  2. Introduction and algorithm definition
  3. Motivation
  4. Introduction
  5. Corals and Coral Reefs
  6. Corals and Reef Formation
  7. Coral reproduction
  8. Reef Longevity and Causes of Death
  9. The Coral Reef Optimization Algorithm
  10. Coral Reef Optimization Algorithm with Substrate Layer
  11. Substrates used in this Thesis
  12. Objectives and main contributions of this work
  13. Structure of the thesis
  14. Engineering Applications
  15. Optimal Battery Scheduling Optimization in Micro-Grids
  16. ...and 35 more sections

Figures (48)

  • Figure 1: CRO-SL reef.
  • Figure 2: Execution diagram about CRO-SL algorithm.
  • Figure 3: Example of evolution of the better(maximum) fitness function of every iteration.
  • Figure 4: Example of percentage about the best production of the substrates.
  • Figure 5: Example of the number of larvae that enter to the reef per substrate.
  • ...and 43 more figures