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Dan Simon

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7 papers

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EAAI Journal 2017 Journal Article

Hybrid invasive weed/biogeography-based optimization

Gholamreza Khademi
Hanieh Mohammadi
Dan Simon

We propose a new variant of the ecologically-inspired optimization method known as invasive weed optimization (IWO). The proposed algorithm features three new components that are typically not present in IWO: (1) migration; (2) gradient descent; and (3) mutation. In standard IWO, each individual uses only its own features (that is, independent solution variables) to randomly distribute new seeds over the search space. In other words, there is no sharing of features among individuals. We propose the application of the migration operator from biogeography-based optimization (BBO) to include the feature-sharing capability in IWO. This modification improves the quality of the distributed seeds (that is, new candidate solutions) in the population. To further improve the local search ability of IWO, we propose the use of gradient descent. Mutation is activated under certain conditions to increase the diversity of the population, and escape local optima. We demonstrate the performance of this new hybrid IWO/BBO on a set of single-objective benchmarks, and on a real-world cyber–physical system problem to optimize a user intent recognition system for transfemoral amputees. Hybrid IWO/BBO is compared to standard IWO, BBO, and 10 other optimization algorithms. The Kruskal–Wallis and Wilcoxon signed-rank tests are used to statistically compare the algorithms. The results for hybrid IWO/BBO present promising improvements over standard IWO. For instance, out of 25 benchmarks, hybrid IWO/BBO performs better than IWO on 18 problems with dimension 30. Hybrid IWO/BBO shows competitive performance with comparison to the 10 other state-of-the-art optimization algorithms.

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EAAI Journal 2015 Journal Article

Biogeography-based optimization of a variable camshaft timing system

George Thomas
Dan Simon
John Michelini

Automotive simulations often prohibit the use of traditional optimization techniques because these simulations are complex and computationally expensive. These two qualities motivate the use of evolutionary algorithms and meta-modeling techniques respectively. In this work, we apply biogeography-based optimization (BBO) to optimize radial basis function (RBF)-based lookup table controls of a variable camshaft timing system for fuel economy. Also, we reduce computational search effort by finding an effective parameterization of the problem, optimizing the parameters of the BBO algorithm for the problem, and estimating the cost of a portion of the candidate solutions in BBO with design and analysis of computer experiments (DACE). We find that we can improve fuel economy by 1. 7% over the original control parameters, and we find a tradeoff in population size, and an optimal value for mutation rate. Finally, we find that we can use a small number of samples to construct DACE models, and we can use these models to estimate a significant portion of the candidate solutions each generation to reduce computation effort and still obtain good BBO solutions.

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EAAI Journal 2015 Journal Article

Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling

Haiping Ma
Shufei Su
Dan Simon
Minrui Fei

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling.

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EAAI Journal 2014 Journal Article

Hybrid biogeography-based evolutionary algorithms

Haiping Ma
Dan Simon
Minrui Fei
Xinzhan Shu
Zixiang Chen

Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The new hybrid EAs include two approaches to hybridization: (1) iteration-level hybridization, in which various EAs and BBO are executed in sequence; and (2) algorithm-level hybridization, which runs various EAs independently and then exchanges information between them using ideas from biogeography. Our empirical study shows that the new hybrid EAs significantly outperforms their constituent algorithms with the selected tuning parameters and generation limits, and algorithm-level hybridization is generally better than iteration-level hybridization. Results also show that the best new hybrid algorithm in this paper is competitive with the algorithms from the 2013 CEC competition. In addition, we show that the new hybrid EAs are generally robust to tuning parameters. In summary, the contribution of this paper is the introduction of biogeography-based hybridization strategies to the EA community.

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EAAI Journal 2013 Journal Article

On the equivalences and differences of evolutionary algorithms

Haiping Ma
Dan Simon
Minrui Fei
Zixiang Chen

Evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popular EAs including genetic algorithm (GA), biogeography-based optimization (BBO), differential evolution (DE), evolution strategy (ES) and particle swarm optimization (PSO). We find that the basic versions of BBO, DE, ES and PSO are equal to the GA with global uniform recombination (GA/GUR) under certain conditions. Then we discuss their differences based on biological motivations and implementation details, and point out that their distinctions enhance the diversity of EA research and applications. To further study the characteristics of various EAs, we compare the basic versions and advanced versions of GA, BBO, DE, ES and PSO to explore their optimization ability on a set of real-world continuous optimization problems. Empirical results show that among the basic versions of the algorithms, BBO performs best on the benchmarks that we studied. Among the advanced versions of the algorithms, DE and ES perform best on the benchmarks that we studied. However, our main conclusion is that the conceptual equivalence of the algorithms is supported by the fact that algorithmic modifications result in very different performance levels.

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EAAI Journal 2011 Journal Article

Analysis of migration models of biogeography-based optimization using Markov theory

Haiping Ma
Dan Simon

Biogeography-based optimization (BBO) is a new evolutionary algorithm inspired by biogeography, which involves the study of the migration of biological species between habitats. Previous work has shown that various migration models of BBO result in significant changes in performance. Sinusoidal migration models have been shown to provide the best performance so far. Motivated by biogeography theory and previous results, in this paper a generalized sinusoidal migration model curve is proposed. A previously derived BBO Markov model is used to analyze the effect of migration models on optimization performance, and new theoretical results which are confirmed with simulation results are obtained. The results show that the generalized sinusoidal migration model is significantly better than other models for simple but representative problems, including a unimodal one-max problem, a multimodal problem, and a deceptive problem. In addition, performance comparison is further investigated through 23 benchmark functions with a wide range of dimensions and diverse complexities, to verify the superiority of the generalized sinusoidal migration model.

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EAAI Journal 2011 Journal Article

Blended biogeography-based optimization for constrained optimization

Haiping Ma
Dan Simon

Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems.

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