A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice

By Kang Seok Lee, Zong Woo Geem. 2005

1,839 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Seyedali Mirjalili
2. Seyed Mohammad Mirjalili
3. Tansel Dökeroğlu
4. Xin-She Yang
5. Amir H. Gandomi
6. Laith Mohammad Abualigah
7. Andrew Lewis
8. Siamak Talatahari

In Computational Theory and Mathematics

1. Seyedali Mirjalili
2. Seyed Mohammad Mirjalili
3. Tansel Dökeroğlu
4. Xin-She Yang
5. Laith Mohammad Abualigah
6. Andrew Lewis
7. Amir H. Gandomi
8. Gaurav Dhiman

In Statistics and Probability

1. Seyed Mohammad Mirjalili
2. Seyedali Mirjalili
3. Andrew Lewis
4. Mehmet Polat Saka
5. Bilal Alatas
6. Ali Kaveh
7. Xin-She Yang
8. Siamak Talatahari

Citations in the corpus

• A survey on new generation metaheuristic algorithms (2019, background): “…then 10 tune the pitch within limits randomly; 11 else 12 generate new harmonics randomly; 13 Find the current best harmonics; Some of the well-known studies of HS are as follows: Lee and Geem (2005) describe an HS algorithm for engineering optimization problems having realnumber design variables.”
• Grey Wolf Optimizer (2014, methodology): “Coello [64] and Deb [65, 66] employed GA, whereas Lee and Geem [67] used HS to solve this problem.”
• Harmony Search as a Metaheuristic Algorithm (2010, methodology): “Since its first appearance in 2001, it has been applied to solve many optimization problems including function optimization, engineering optimization [2], water distribution networks [3], groundwater”
• pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization (2011, positive): “This Python code is an extension of a harmony search optimizer (Geem et al. 2001; Lee and Geem 2005) that handles constraints.”
• Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (2015, methodology): “Some of the works that solved this problem are as follows: GA [31–33], CPSO [34], HS [35], Richardson’s random method, Simplex method, Davidon–Fletcher–Powell, and Griffith and Stewart’s successive linear approximation [36].”
• Modified Grey Wolf Optimizer for Global Engineering Optimization (2016, background): “…which is treated as in between genetic and culture evolution (Memetic Algorithm [11]), harmony of music played by musicians (Harmony Search [12, 13]), cultural behavior of frogs (Shuffled Frog-Leaping Algorithm [14]), Gravitational Search algorithm [15], Multiverse Optimizer (MVO) [16], and…”
• Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems (2008, methodology): “006 harmony is called ‘improvisation’ [18].”