scispace - formally typeset
Search or ask a question
Author

Andrew Lewis

Bio: Andrew Lewis is an academic researcher from Griffith University. The author has contributed to research in topics: Particle swarm optimization & Population. The author has an hindex of 29, co-authored 154 publications receiving 13604 citations. Previous affiliations of Andrew Lewis include Queensland Institute of Business and Technology & Information Technology University.


Papers
More filters
Journal ArticleDOI
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.

10,082 citations

Journal ArticleDOI
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.

7,090 citations

Journal ArticleDOI
TL;DR: The proposed grasshopper optimisation algorithm is able to provide superior results compared to well-known and recent algorithms in the literature and the results of the real applications prove the merits of GOA in solving real problems with unknown search spaces.

1,796 citations

Journal ArticleDOI
TL;DR: Six new transfer functions divided into two families, s-shaped and v-shaped, are introduced and evaluated and prove that the new introduced v- shaped family of transfer functions significantly improves the performance of the original binary PSO.
Abstract: Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost makes this algorithm very popular and powerful in solving a wide range of problems. The binary version of this algorithm has been introduced for solving binary problems. The main part of the binary version is a transfer function which is responsible to map a continuous search space to a discrete search space. Currently there appears to be insufficient focus on the transfer function in the literature despite its apparent importance. In this study six new transfer functions divided into two families, s-shaped and v-shaped, are introduced and evaluated. Twenty-five benchmark optimization functions provided by CEC 2005 special session are employed to evaluate these transfer functions and select the best one in terms of avoiding local minima and convergence speed. In order to validate the performance of the best transfer function, a comparative study with six recent modifications of BPSO is provided as well. The results prove that the new introduced v-shaped family of transfer functions significantly improves the performance of the original binary PSO.

766 citations

Journal ArticleDOI
TL;DR: Training MLPs by using Biogeography-Based Optimization is significantly better than the current heuristic learning algorithms and BP, and the results show that BBO is able to provide very competitive results in comparison with ELM.

261 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.

10,082 citations

01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.

7,090 citations

Journal ArticleDOI
TL;DR: The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces.
Abstract: This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html .

3,088 citations

Journal ArticleDOI
TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.

3,027 citations