Journal ArticleDOI
Discrete optimisation of a gear train using biogeography based optimisation technique
TLDR
In this paper, a new global optimisation algorithm, biogeography based optimisation (BBO), for solving discrete optimisation of a gear train is presented, where the objective considered is minimisation of weight.Abstract:
In this paper, a new global optimisation algorithm, biogeography based optimisation (BBO), for solving discrete optimisation of a gear train is presented. The efficiency and ease of application of the proposed optimisation algorithm is demonstrated by solving a discrete optimisation problem of a four stage gear train from the literature. The objective considered is minimisation of weight. Eighty six inequality constraints are considered which include, bending fatigue strength, contact strength, contact ratio, pinion/gear size, housing size, pitch for gears and kinematic constraints. Twenty two discrete design variables are considered in the optimisation. Design modification is done to reduce the design variables which include two different designs with 18 and 14 design variables. The results of the proposed method are compared with the results obtained by using other optimisation methods such as genetic algorithm, particle swarm optimisation (PSO), and differential evolution (DE). The solution obtained by using BBO is superior to those obtained by using other optimisation techniques.read more
Citations
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Journal ArticleDOI
Markov Models for Biogeography-Based Optimization
TL;DR: This paper derives Markov models for BBO with selection, migration, and mutation operators that give the theoretically exact limiting probabilities for each possible population distribution for a given problem.
Journal ArticleDOI
Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms
TL;DR: It is shown that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR) and that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems.
Journal ArticleDOI
Linearized biogeography-based optimization with re-initialization and local search
TL;DR: A linearized version of BBO, called LBBO, is proposed to reduce rotational variance and performs particularly well for certain types of multimodal problems, including high-dimensional real-world problems.
Journal ArticleDOI
Effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO)
TL;DR: Results show that proposed hybridization of BBO with ACO and AIA is effective over a wide range of problems and is also effective over other proposed hybridized BBO and different variants of B BO available in the literature.
Journal ArticleDOI
A dynamic system model of biogeography-based optimization
TL;DR: A dynamic system model for biogeography-based optimization (BBO) is derived that is asymptotically exact as the population size approaches infinity and generally results in better optimization results than GAs for the problems that are investigated.
References
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Ant system: optimization by a colony of cooperating agents
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Journal ArticleDOI
Biogeography-Based Optimization
TL;DR: This paper discusses natural biogeography and its mathematics, and then discusses how it can be used to solve optimization problems, and sees that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO).
Journal ArticleDOI
Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization
TL;DR: Experimental results in terms of the likelihood of convergence to a global optimal solution and the solution speed suggest that the SFLA can be an effective tool for solving combinatorial optimization problems.
A combined genetic adaptive search (GeneAS) for engineering design
Kalyanmoy Deb,Mayank Goyal +1 more
TL;DR: The proposed technique is compared with binarycoded genetic algorithms, Augmented Lagrange multiplier method, Branch and Bound method and Hooke and Jeeves pattern search method and the solutions obtained are superior than those obtained with other methods.
Journal ArticleDOI
Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem
TL;DR: The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.