A Swarm Metaphor for Multiobjective Design Optimization

By Tapabrata Ray, K. Liew. 2002

401 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1bedc8b2b61cc8af25b4fd2582b82b54a68c52b5

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In Artificial Intelligence

In Civil and Structural Engineering

In Computational Theory and Mathematics

Citations in the corpus

An improved particle swarm optimizer for mechanical design optimization problems (2004, methodology): “Other attempts include applying a multi-objective optimization technique to handle constraints [23].”
Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems (2016, background): “Minimize: f1(x) = 0.7854∗x(1)∗x(2)2∗(3.3333 ∗ x(3)2 +14.9334 ∗ x(3))... − 43.0934) − 1.508 ∗ x(1) ∗ (x(6)2 + x(7)2 + 7.4777 ∗ (x(6)3 + x(7)3)... (C.3) + 0.7854 ∗ (x(4) ∗ x(6)2 + x(5) ∗ x(7)2) Minimize: f2(x) = ((sqrt (((745 ∗ x(4))/x(2) ∗ x(3)))2 +19.9e6))/(0.1 ∗ x(6)3)) (C.4) Where: g1(x) = 27/(x(1”
Multiobjective cuckoo search for design optimization (2013, background): “10, where we can see that the results are smooth and are the same or better than the results obtained in [32].”
Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance (2005, methodology): “We will review next the most representative of them: Ray and Liew [ 4 ]: This algorithm uses Pareto dominance and combines concepts of evolutionary techniques with the particle swarm.”
An easy-to-use real-world multi-objective optimization problem suite (2020, methodology): “CRE2-3-1 Two bar truss design [27] RE3-3-1 2 3 3 Continuous CRE2-4-2 Welded beam design [28] RE3-4-2 2 4 4 Continuous CRE2-4-3 Disc brake design [28] RE3-4-3 2 4 4 Continuous CRE2-7-4 Speed reducer design [30] RE3-7-5 2 7 11 Mixed CRE2-4-5 Gear train design [31] RE3-4-6 2 4 1 Integer CRE3-7-1 Car si”
Multi-Objective Flower Algorithm for Optimization (2014, positive): “Nature-inspired algorithms have shown their promising performance and have thus become popular and widely used, and these algorithms are mostly swarm intelligence based [4, 8, 22, 29, 30, 31, 32, 12, 13].”
Handling multiple objectives with particle swarm optimization (2004, methodology): “Liew [28]: This algorithm also uses Pareto dominance and combines concepts of evolutionary techniques with the particle swarm.”
An effective use of crowding distance in multiobjective particle swarm optimization (2005, methodology): “Several techniques have been incorporated into multiobjective optimization algorithms especially to PSO-based algorithms in order to improve convergence to the true Pareto front as well as produce a well-distributed Pareto front.”
Flower pollination algorithm: A novel approach for multiobjective optimization (2014, methodology): “The detailed formulation can be found in (Deb 1999; Ray and Liew 2002; Gong et al. 2009).”
Bat algorithm for multi-objective optimisation (2011, background): “This is consistent with the results obtained by others (Ray and Liew, 2002; Pham and Ghanbarzadeh, 2007).”