Hybrid biogeography-based evolutionary algorithms

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

Biogeography-Based Optimization for Combinatorial Problems and Complex Systems

Biogeography-based optimization (BBO) is a heuristic evolutionary algorithm that has shown good performance on many problems. In this dissertation, three problem1s 1 are researched for BBO: convergence speed and optimal solution convergence of BBO,1 1BBO application to combinatorial problems, and BBO application to complex systems. The first problem is to analyze BBO from two perspectives: how the components of BBO affect its convergence speed and the reason that BBO converges to the optimal solution. For the first perspective, which is convergence speed, we analyze the two essential components of BBO -- population construction and information sharing. For the second perspective, a mathematical BBO model is built to theoretically prove why BBO is capable of reaching the global optimum for any problem. In the second problem addressed by the dissertation, BBO is applied to combinatorial problems. Our research includes the study of migration, local search, population initialization, and greedy methods for combinatorial problems. We conduct a series of simulations based on four benchmarks, the sizes of which vary from small to extra large. The simulation results indicate that when combined with other techniques, the performance of BBO can be significantly improved. Also, a BBO graphical user interface (GUI) is created for combinatorial problems, which is an intuitive way to experiment with BBO algorithms, including hybrid BBO algorithms. The third and final problem addressed in this dissertation is the optimization of complex systems. We invent a new algorithm for complex system optimization based on BBO, which is called BBO/complex. Four real world problems are used to test BBO/Complex and compare with other complex system optimization algorithms, and we obtain encouraging results from BBO/Complex. Then, a Markov model is created for BBO/Complex. Simulation results are provided to confirm the mode

Biogeography-Based Optimization: Synergies with Evolutionary Strategies, Immigration Refusal, and Kalman Filters

Biogeography-based optimization (BBO) is a recently developed heuristic algorithm which has shown impressive performance on many well known benchmarks. The aim of this thesis is to modify BBO in different ways. First, in order to improve BBO, this thesis incorporates distinctive techniques from other successful heuristic algorithms into BBO. The techniques from evolutionary strategy (ES) are used for BBO modification. Second, the traveling salesman problem (TSP) is a widely used benchmark in heuristic algorithms, and it is considered as a standard benchmark in heuristic computations. Therefore the main task in this part of the thesis is to modify BBO to solve the TSP, then to make a comparison with genetic algorithms (GAs). Third, most heuristic algorithms are designed for noiseless environments. Therefore, BBO is modified to operate in a noisy environment with the aid of a Kalman filter. This involves probability calculations, therefore BBO can choose the best option in its immigration ste

Biogeography-Based Optimization: Synergies with Evolutionary Strategies, Immigration Refusal, and Kalman Filters

Biogeography-based optimization (BBO) is a recently developed heuristic algorithm which has shown impressive performance on many well known benchmarks. The aim of this thesis is to modify BBO in different ways. First, in order to improve BBO, this thesis incorporates distinctive techniques from other successful heuristic algorithms into BBO. The techniques from evolutionary strategy (ES) are used for BBO modification. Second, the traveling salesman problem (TSP) is a widely used benchmark in heuristic algorithms, and it is considered as a standard benchmark in heuristic computations. Therefore the main task in this part of the thesis is to modify BBO to solve the TSP, then to make a comparison with genetic algorithms (GAs). Third, most heuristic algorithms are designed for noiseless environments. Therefore, BBO is modified to operate in a noisy environment with the aid of a Kalman filter. This involves probability calculations, therefore BBO can choose the best option in its immigration ste

Biogeography-Based Optimization: Synergies with Evolutionary Strategies, Immigration Refusal, and Kalman Filters

Biogeography-based optimization (BBO) is a recently developed heuristic algorithm which has shown impressive performance on many well known benchmarks. The aim of this thesis is to modify BBO in different ways. First, in order to improve BBO, this thesis incorporates distinctive techniques from other successful heuristic algorithms into BBO. The techniques from evolutionary strategy (ES) are used for BBO modification. Second, the traveling salesman problem (TSP) is a widely used benchmark in heuristic algorithms, and it is considered as a standard benchmark in heuristic computations. Therefore the main task in this part of the thesis is to modify BBO to solve the TSP, then to make a comparison with genetic algorithms (GAs). Third, most heuristic algorithms are designed for noiseless environments. Therefore, BBO is modified to operate in a noisy environment with the aid of a Kalman filter. This involves probability calculations, therefore BBO can choose the best option in its immigration ste

Application of opposition-based learning concepts in reducing the power consumption in wireless access networks

The reduction of power consumption in wireless access networks is a challenging and important issue. In this paper, we apply Opposition-Based Learning (OBL) concepts for reducing the power consumption of LTE base stations. More specifically, we present a new Modified Biogeography Based Optimization (BBO) algorithm enhanced with OBL techniques. We apply both the original BBO and the new Modified Opposition BBO (MOBBO) to network design cases to the city of Ghent, Belgium, with 75 possible LTE base station locations. We optimize the network towards two objectives: coverage maximization and power consumption minimization. Preliminary results indicate the advantages and applicability of our approach

A novel metaheuristic for traveling salesman problem: blind mole-rat algorithm

Traveling Salesman Problem (TSP) is the problem of finding a minimum distance tour of cities beginning and ending at the same city and that each city are visited only once. As the number of cities increases, it is difficult to find an optimal solution by exact methods in a reasonable duration. Therefore, in recent five decades many heuristic solution methods inspired of nature and biology have been developed. In this paper, a new metaheuristic method inspired of the by-passing the obstacle strategy of blind mole rats living in their individual tunnel systems under the soil is designed for solving TSP. The method is called as Blind Mole-rat Algorithm. The proposed algorithm is tested on different size of symmetric TSP problems and the results are compared to the best known results. Initial test results are promising although proposed metaheuristic is not yet competitive enough among other algorithms in the literature

A novel metaheuristic for traveling salesman problem: blind mole-rat algorithm

Gezgin Satıcı Problemi (GSP), başlangıç ve bitiş şehirleri aynı olan ve her şehrin sadece bir kez ziyaret edildiği minimum mesafeli turu bulma problemidir. Şehir sayısı arttıkça, kesin yöntemler ile kabul edilebilir sürelerde bir optimum çözüm bulunması zordur. Bu nedenle, son elli yılda GSP’nin çözümü için doğadan ve biyolojiden esinlenen birçok meta-sezgisel yöntem geliştirilmiştir. Bu çalışmada, toprak altındaki bireysel tünel sistemlerinde yaşayan kör farelerin toprak altındaki engelleri geçme stratejisinden esinlenilerek GSP’nin çözümü için yeni bir meta-sezgisel tasarlanmıştır. Geliştirilen yönteme Kör Fare Algoritması adı verilmiştir. Bu yeni sezgisel ile farklı boyutlardaki simetrik test veri setleri için deneyler yapılmış ve sonuçları bilinen en iyi sonuçlar ile kıyaslanmıştır. Önerilen meta-sezgisel henüz literatürdeki diğer algoritmalarla yarışabilecek düzeyde olmamasına rağmen, başlangıç test çözümlerinin umut verici olduğu söylenebilir.Traveling Salesman Problem (TSP) is the problem of finding a minimum distance tour of cities beginning and ending at the same city and that each city are visited only once. As the number of cities increases, it is difficult to find an optimal solution by exact methods in a reasonable duration. Therefore, in recent five decades many heuristic solution methods inspired of nature and biology have been developed. In this paper, a new metaheuristic method inspired of the by-passing the obstacle strategy of blind mole rats living in their individual tunnel systems under the soil is designed for solving TSP. The method is called as Blind Mole-rat Algorithm. The proposed algorithm is tested on different size of symmetric TSP problems and the results are compared to the best known results. Initial test results are promising although proposed metaheuristic is not yet competitive enough among other algorithms in the literature

Ensemble Multi-Objective Biogeography-Based Optimization with Application to Automated Warehouse Scheduling

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