Tackling Blind Spot Challenges in Metaheuristics Algorithms Through Exploration and Exploitation
This paper defines blind spots in continuous optimization problems as global optima that are inherently difficult to locate due to deceptive, misleading, or barren regions in the fitness landscape. Such regions can mislead the search process, trap metaheuristic algorithms (MAs) in local optima, or h...
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MDPI AG
2025-05-01
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| author | Matej Črepinšek Miha Ravber Luka Mernik Marjan Mernik |
| author_facet | Matej Črepinšek Miha Ravber Luka Mernik Marjan Mernik |
| author_sort | Matej Črepinšek |
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| description | This paper defines blind spots in continuous optimization problems as global optima that are inherently difficult to locate due to deceptive, misleading, or barren regions in the fitness landscape. Such regions can mislead the search process, trap metaheuristic algorithms (MAs) in local optima, or hide global optima in isolated regions, making effective exploration particularly challenging. To address the issue of premature convergence caused by blind spots, we propose LTMA+ (Long-Term Memory Assistance Plus), a novel meta-approach that enhances the search capabilities of MAs. LTMA+ extends the original Long-Term Memory Assistance (LTMA) by introducing strategies for handling duplicate evaluations, shifting the search away from over-exploited regions and dynamically toward unexplored areas and thereby improving global search efficiency and robustness. We introduce the Blind Spot benchmark, a specialized test suite designed to expose weaknesses in exploration by embedding global optima within deceptive fitness landscapes. To validate LTMA+, we benchmark it against a diverse set of MAs selected from the EARS framework, chosen for their different exploration mechanisms and relevance to continuous optimization problems. The tested MAs include ABC, LSHADE, jDElscop, and the more recent GAOA and MRFO. The experimental results show that LTMA+ improves the success rates for all the tested MAs on the Blind Spot benchmark statistically significantly, enhances solution accuracy, and accelerates convergence to the global optima compared to standard MAs with and without LTMA. Furthermore, evaluations on standard benchmarks without blind spots, such as CEC’15 and the soil model problem, confirm that LTMA+ maintains strong optimization performance without introducing significant computational overhead. |
| format | Article |
| id | doaj-art-73e598aa9ae44062b60840fb4a4c914c |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-73e598aa9ae44062b60840fb4a4c914c2025-08-20T01:56:31ZengMDPI AGMathematics2227-73902025-05-011310158010.3390/math13101580Tackling Blind Spot Challenges in Metaheuristics Algorithms Through Exploration and ExploitationMatej Črepinšek0Miha Ravber1Luka Mernik2Marjan Mernik3Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, SloveniaDepartment of Applied Mathematics, Florida Polytechnic University, 4700 Research Way, Lakeland, FL 33805, USAFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, SloveniaThis paper defines blind spots in continuous optimization problems as global optima that are inherently difficult to locate due to deceptive, misleading, or barren regions in the fitness landscape. Such regions can mislead the search process, trap metaheuristic algorithms (MAs) in local optima, or hide global optima in isolated regions, making effective exploration particularly challenging. To address the issue of premature convergence caused by blind spots, we propose LTMA+ (Long-Term Memory Assistance Plus), a novel meta-approach that enhances the search capabilities of MAs. LTMA+ extends the original Long-Term Memory Assistance (LTMA) by introducing strategies for handling duplicate evaluations, shifting the search away from over-exploited regions and dynamically toward unexplored areas and thereby improving global search efficiency and robustness. We introduce the Blind Spot benchmark, a specialized test suite designed to expose weaknesses in exploration by embedding global optima within deceptive fitness landscapes. To validate LTMA+, we benchmark it against a diverse set of MAs selected from the EARS framework, chosen for their different exploration mechanisms and relevance to continuous optimization problems. The tested MAs include ABC, LSHADE, jDElscop, and the more recent GAOA and MRFO. The experimental results show that LTMA+ improves the success rates for all the tested MAs on the Blind Spot benchmark statistically significantly, enhances solution accuracy, and accelerates convergence to the global optima compared to standard MAs with and without LTMA. Furthermore, evaluations on standard benchmarks without blind spots, such as CEC’15 and the soil model problem, confirm that LTMA+ maintains strong optimization performance without introducing significant computational overhead.https://www.mdpi.com/2227-7390/13/10/1580optimizationmetaheuristics algorithmalgorithmic performanceduplicate solutionsnon-revisited solutionsLTMA |
| spellingShingle | Matej Črepinšek Miha Ravber Luka Mernik Marjan Mernik Tackling Blind Spot Challenges in Metaheuristics Algorithms Through Exploration and Exploitation Mathematics optimization metaheuristics algorithm algorithmic performance duplicate solutions non-revisited solutions LTMA |
| title | Tackling Blind Spot Challenges in Metaheuristics Algorithms Through Exploration and Exploitation |
| title_full | Tackling Blind Spot Challenges in Metaheuristics Algorithms Through Exploration and Exploitation |
| title_fullStr | Tackling Blind Spot Challenges in Metaheuristics Algorithms Through Exploration and Exploitation |
| title_full_unstemmed | Tackling Blind Spot Challenges in Metaheuristics Algorithms Through Exploration and Exploitation |
| title_short | Tackling Blind Spot Challenges in Metaheuristics Algorithms Through Exploration and Exploitation |
| title_sort | tackling blind spot challenges in metaheuristics algorithms through exploration and exploitation |
| topic | optimization metaheuristics algorithm algorithmic performance duplicate solutions non-revisited solutions LTMA |
| url | https://www.mdpi.com/2227-7390/13/10/1580 |
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