Impacts of Linnik Flight Usage Patterns on Cuckoo Search for Real-Parameter Global Optimization Problems

• Published in: IEEE access 2019, Vol.7, p.83932-83961
• Main Authors: Nasa-Ngium, Patchara, Sunat, Khamron, Chiewchanwattana, Sirapat
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial bee colony algorithm; Birds; Cuckoo search; Evolutionary computation; Global optimization; heavy-tailed distribution; Indexes; Linnik flight; Lévy flight; nature-inspired algorithm; Optimization; optimization problems; Particle swarm optimization; Search algorithms; Search problems; Stochastic processes; Use statistics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2923557

Several contemporary algorithms, including cuckoo search (CS), were applied to the CEC 2017 problem set, which includes a wide variety of 120 very difficult subproblems. We found that the algorithms were ineffective, especially when the number of dimensions was high. We configured several usage patterns of Linnik flight with the inverse of the golden ratio (1/Φ) to replace Lévy flight in CS, resulting in a new search mechanism that increased the efficiency of the CS algorithm. The impacts of each Linnik flight usage pattern were evaluated using the CEC 2017. The experimental results showed that: 1) CS variants that used Linnik flight were more capable than CS variants that used Lévy flight and 2) CS variants that used a mixture of Linnik flight and quantum-behaved mechanisms were even more capable. The primary effect of Linnik flight is the strengthening of the ranking, while that of the quantum-behaved mechanisms is a decreased error. A chaotic-initialized quantum-Linnik flight CS (CQLCS) algorithm is proposed. Among the 66 competitive methods applied to the CEC 2017, CQLCS ranked first and won the contemporary competitive algorithms section, which also included several advanced variants of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The CQLCS could potentially be improved further by adjusting the probability of the occurrence of Linnik flight. The processes for building improved variants were analyzed to discover how, or if, this improvement could be achieved. Finally, the CQLCS algorithm required fewer lines of code to run than did the DE and PSO variants.