Biogeography-based optimization for identifying promising compounds in chemical process

This chapter introduces the basic concepts of biogeography based optimization (BBO) algorithm and its application to a combinatorial water switching problem. Water switching optimization is a pump scheduling problem which considers minimization of total electrical energy requirement as an objective function. Pump status (switch on/switch off) of pumping stations are considered as a discrete (binary) decision variables for the optimization problem. Suction and discharge pressure are considered as constraints in the procedure. A case study with 10 pumping station and 40 pumps is presented for the experimentation. The performance of BBO is tested against other state-of-the art algorithms that includes genetic algorithm (GA), branch & bound method (B&B), harmony search (HS) algorithm, particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms. Water pump switching problem is also investigated by using different constraint handling techniques and by considering different pump situations in a pumping station. The Computational results indicate that BBO is an appropriate algorithm to solve water pump switching problem and is effective over other optimization methods. Moreover, 20 alternative optimum solutions are presented to demonstrate water switching problem as a multi-modal problem with different optimum solutions and the search capability of BBO to find alternate optimum solutions.

Biogeography-based optimization (BBO) is an evolutionary algorithm that is based on the science of biogeography. Biogeography is the study of the geographical distribution of organisms. In BBO, problem solutions are represented as islands, and the sharing of features between solutions is represented as migration between islands. This paper develops a Markov analysis of BBO, including the option of elitism. Our analysis gives the probability of BBO convergence to each possible population distribution for a given problem. We compare our BBO Markov analysis with a similar genetic algorithm (GA) Markov analysis. Analytical comparisons on three simple problems show that with high mutation rates the performance of GAs and BBO is similar, but with low mutation rates BBO outperforms GAs. Our analysis also shows that elitism is not necessary for all problems, but for some problems it can significantly improve performance.

Biogeography-based optimization (BBO) is a powerful evolutionary algorithm inspired from the science of biogeography. It mainly uses the biogeography-based migration operator to share the information among individuals. In canonical BBO, according to the principle of immigration and emigration, poor solutions are like to be completely replaced by better ones. Consequently, this will lead to reduction of the population diversity. On the other hand, for Particle Swarm Optimization, a particle will learn from the global best solution and its own history best solution, which also deteriorate population diversity. In this paper, Particle Swarm Optimization (PSO) employs the selection mechanism of BBO and provides its solution updating strategy for BBO. A good particle has a large probability to be learned, while a poor particle has a small probability to be learned. In this way, the whole swarm can eliminate the affects from only one solution. The simulation is done using fourteen benchmark functions, and the results demonstrate that this hybrid BBO-PSO algorithm works efficiently.

An analysis of the migration rates for biogeography-based optimization

Technology has enabled us to carry the world on our tips. Cloud computing has majorly contributed to this by providing infrastructure services on the go using pay per use model and with high quality of services. Cloud services provide resources through various distributed datacenters and client requests been fulfilled over these datacenters which act as resources. Therefore, resource allocation plays an important role in providing a high quality of service like utilization, network delay and finish time. Biogeography-based optimization (BBO) is an optimization algorithm that is an evolutionary algorithm used to find optimized solution. In this work BBO algorithm is been used for resource optimization problem in cloud environment at infrastructure as a service level. In past several task scheduling algorithms are being proposed to find a global best schedule to achieve least execution time and high performance like genetic algorithm, ACO and many more but as compared to GA, BBO has high probability to find global best solution. Existing solutions aim toward improving performance in term of power execution time, but they have not considered network performance and utilization of the systems performance parameters. Therefore, to improve the performance of cloud in network-aware environment we have proposed an efficient nature inspired BBO algorithm. Further, the proposed approach takes network overhead and utilization of the system into consideration to provide improved performance as compared to ACO, Genetic algorithm as well as with PSO.

Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by the migration of species between habitats. Almost 10 years have passed since the first BBO paper was published in 2008. BBO has successfully solved optimization problems in many different domains and has reached a relatively mature state. Considering the significant and expanding research on BBO and its applications, we find that the time is right to provide a 10-year anniversary review of the published literature, and also to point out some important avenues of future research. The purpose of this paper is to summarize and organize the literature related to the past 10 years of BBO research. Beginning with a foundation of basic BBO, we review the family of BBO algorithms and discuss BBO modifications, hybridizations, applications in science and engineering, and mathematical theory. Finally, the paper presents some interesting open problems and future research directions for BBO.

This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms.

Biogeography-based optimization (BBO), a recent proposed metaheuristic algorithm, has been successfully applied to many optimization problems due to its simplicity and efficiency. However, BBO is sensitive to the curse of dimensionality; its performance degrades rapidly as the dimensionality of the search space increases. In this paper, a selective migration operator is proposed to scale up the performance of BBO and we name it selective BBO (SBBO). The differential migration operator is selected heuristically to explore the global area as far as possible whist the normal distributed migration operator is chosen to exploit the local area. By the means of heuristic selection, an appropriate migration operator can be used to search the global optimum efficiently. Moreover, the strategy of cooperative coevolution (CC) is adopted to solve large-scale global optimization problems (LSOPs). To deal with subgroup imbalance contribution to the whole solution in the context of CC, a more efficient computing resource allocation is proposed. Extensive experiments are conducted on the CEC 2010 benchmark suite for large-scale global optimization, and the results show the effectiveness and efficiency of SBBO compared with BBO variants and other representative algorithms for LSOPs. Also, the results confirm that the proposed computing resource allocation is vital to the large-scale optimization within the limited computation budget.

In the chemical process, the uncertainties are always encountered. Therefore, the algorithm of process modeling, simulation, optimization, and control should have the capability to handle the uncertain parameter. Meta-Heuristics Optimization (MO) techniques are attractive global optimization methods inspired by the various industrial phenomena with uncertainty. These methods have been successfully applied to a wide range of chemical engineering problems with a higher level of degree of satisfaction. In this chapter, the authors introduce multiple artificial intelligence techniques: Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Differential Evolution (DE), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), Stud Genetic Algorithm (SGA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Fuzzy Logic (FL). It includes the introduction of algorithms and their applications to handle the uncertainty in the chemical process operation.

As a novel Evolutionary Algorithm (EA), Biogeography-Based Optimization (BBO), inspired by the science of biogeography, draws much attention due to its significant performance in both numerical simulations and practical applications. In BBO, the features in poor solutions have a large probability to be replaced by the features in good solutions. The replacement operator is termed migration. However, the replacement causes a loss of the features in poor solutions, breaks the diversity of population and may lead to a local optimal solution. To overcome this, we design a novel migration operator to propose Backtracking BBO (BBBO). In BBBO, besides the regular population, an external population is employed to record historical individuals. The size of external population is the same as the size of regular population. The external population and regular population are used together to generate the next population. After that, the individuals in external population are randomly selected to be updated by the individuals in current population. In this way, the external population in BBBO can be considered as a memory to take part in the evolutionary process. The memory takes into account both current and historical data to generate next population, which enhances algorithm's ability in exploring searching space. In numerical simulation, 14 classical benchmarks are employed to test BBBO's performance and several classical nature inspired algorithms are use in comparison. The results show that the strategy in BBBO is feasible and very effective to enhance algorithm's performance. In addition, we apply BBBO to mechanical design problems which involve constraints in optimization. The comparison results also exhibit that BBBO is very competitive in solving practical optimization problems.

This paper shows the usefulness of soft computing techniques in antenna parameters calculation, design and optimization. Since long time, different soft computing techniques have been drawn attention of researchers, such as Artificial neural network (ANNs), Fuzzy Logic, Radial basis function neural network (RBFNNs) and evolutionary algorithms (EAs). Some popular EAs are Genetic algorithm and its variants, Particle swarm optimization (PSO), Ant Colony, Differential Evolution, Bacterial Foraging Optimization (BFO) and Biogeography Based Optimization (BBO). The main focus in this paper is to review implementation and optimization of different antenna structures through different optimization techniques.

Biogeography-based Optimization (BBO) Algorithm for Single Machine Total Weighted Tardiness Problem (SMTWTP)

Proportional Integral Derivative (PID) controller is commonly used in process control systems. Tuning PID controller parameters are a very challenging problem to improve performance and stability of a process. This paper presents a novel method for PID controller tuning problem using Linearized Biogeography-Based Optimization (LBBO) algorithm. Biogeography-Based Optimization (BBO) is an evolutionary optimization algorithm based on mathematical model of Biogeography, it permits of sharing the features among candidate solutions (habitats) represented by emigration and immigration. By using Matlab/Simulink and the squared error integral criterion as objective function. The algorithm is applied to benchmarks functions optimization design, and is then compared with Particle Swarm Optimization (PSO), BBO, and Modified Biogeography-Based Optimization (MBBO). Simulation results shown that the LBBO is an effective tuning method and has better performance compared with PSO, BBO, and MBBO.

Benchmarking the Clustering Performances of Evolutionary Algorithms: A Case Study on Varying Data Size

Fuzzy C-means (FCM) clustering method has been widely used in image segmentation that plays an important role in a variety of applications in image processing and computer vision systems, but the performance of FCM heavily relies on the initial cluster centers which are difficult to determine. To solve the problem, the paper proposes a new hybrid method for image segmentation, which first randomly generates a population of initial clustering solutions and then uses an evolutionary algorithm to search for better clustering solutions; at each iteration, the FCM method is applied on each initial clustering solution to produce its segmentation result. Among a set of popular evolutionary algorithms, we find that the biogeography-based optimization (BBO) metaheuristic exhibits good performance on the considered problem. Besides the basic BBO, we have also proposed a set of improved BBO versions in their combination with FCM for image segmentation. Computational experiments on a set of test images show that the proposed method has significant advantage over the basic FCM algorithm and those hybrid algorithms combining FCM with other evolutionary algorithms such as artificial bee colony and particle swarm optimization.