The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

Implementation analysis of cuckoo search for the benchmark rosenbrock and levy test functions

This paper presents the implementation analysis of the benchmark Rosenbrock and Levy test functions using the Cuckoo Search with emphasis on the effect of the search population and iterations count in the algorithm’s search processes. After many experimental procedures, this study revealed that deploying a population of 10 nests is sufficient to obtain acceptable solutions to the Rosenbrock and Levy test functions (or any similar problem to these test landscapes). In fact, increasing the search population to 25 or more nests was a demerit to the Cuckoo Search as it resulted in increased processing overhead without any improvement in processing outcomes. In terms of the iteration count, it was discovered that the Cuckoo Search could obtain satisfactory results in as little as 100 iterations. The outcome of this study is beneficial to the research community as it helps in facilitating the choice of parameters whenever one is confronted with similar problems

Implementation evaluation of Cuckoo search for the benchmark Rosenbrock test function

This paper presents the implementation evaluation of the benchmark Rosenbrock test function with particular emphasis on the effect of the search population and iterations count in the Cuckoo Search algorithm's quest for good solutions. After a number of experimental procedures, this study reveals that deploying a population of 10 nests is sufficient to obtain good solutions to the Rosenbrock test function (or any similar problem to this test landscape). In fact, increasing the search population to 50 nests was a demerit to the Cuckoo Search as it resulted in longer processing time and worse outcomes. In terms of the iteration count, it was discovered that the Cuckoo Search can obtain good results in as little as 100 to 10,000 iterations. The outcome of this study is beneficial to the research community as it will help in facilitating the choice of parameters whenever one is confronted with a similar problem

A new fitness function for tuning parameters of Peripheral Integral Derivative Controllers

In recent years, the need for greater effectiveness and efficiency in industrial processes and procedures has made control system engineering a favored area of scientific investigation, especially the proper tuning of the parameters of Peripheral Integral Derivative (PID) Controllers. This paper critically examines some issues with the present fitness functions being used by different researchers involved in metaheuristic tuning of PID Controllers. It concludes with the introduction of a fitness function called the Inverse Integrated Squared Absolute Error whose experimental outcome using the African Buffalo Optimization algorithm was able to obtain zero (0) steady state error, zero (0) overshoot, 1.77 s rise time and 2.87 s steady time which is quite competitive. The paper opines that further appropriate investigations of the metaheuristic tuning of PID Controllers using this latest fitness function are highly recommended since it is very simple to both implement and use

Stochastic process and tutorial of the African bufalo optimization

This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm’s basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithm

Swarm Intelligence Optimization Algorithms: A Review

Swarm Intelligence, of late, has gradually become an exciting area of research interest to many researchers in science and engineering. The primary reason for this interest is because swarm intelligence exploits the miraculous cum harmonious working of nature in ensuring order, preservation, conservation, longevity and sustenance of plants and animals in the ecosystem. As a result, researchers that believe that mimicking nature is key to solving diverse problems in engineering, technology and science has developed a number of swarm intelligence techniques. This paper presents a review of some recently developed swarm intelligence algorithms that have been successfully applied to solve a number of optimization problems with special emphasis on their application areas, strengths and observable weaknesses. This study aims to assist researchers in their choice of algorithm to solve optimization problems

African Buffalo Optimization Algorithm Based T-Way Test Suite Generation Strategy for Electronic-Payment Transactions

The use of meta-heuristics in Combinatorial Interaction Testing (CIT) is becoming more and more popular due to their effectiveness and efficiency over the traditional methods espe-cially in authenticating electronic payment (e-payment) transactions. Concomitantly, over the past two decades, there has been a rise both in the development of metaheuristics and their application to diverse theoretical and practical areas including CIT in e-payments. In the implementation of t-way strategies (the t is used to represent the interaction strength), mixed results have been reported; some very exciting but, in other cases, the performance of metaheuristics has been, to say the least, below par. This mixed trend has led many re-searchers to explore alternate ways of improving the effectiveness and efficiency of me-taheuristics in CIT, hence this study. It must be emphasized, however, that available litera-ture indicates that no particular metaheuristic testing strategy has had consistent superior performance over the others in diverse testing environments and configurations. The need for effectiveness, therefore, necessitates the need for algorithm hybridization to deploy only the component parts of algorithms that have been proven to enhance overall search capa-bilities while at the same time eliminating the demerits of particular algorithms in the hybrid-ization procedure. In this paper, therefore, a hybrid variant of the African Buffalo Optimi-zation (ABO) algorithm is proposed for CIT. Four hybrid variants of the ABO are proposed through a deliberate improvement of the ABO with four algorithmic components. Experi-mental procedures indicate that the hybridization of the ABO with these algorithmic com-ponents led to faster convergence and greater effectiveness superior to the outcomes of existing techniques, thereby placing the algorithm among the best when compared with other methods/techniques

BVAGQ-AR for Fragmented Database Replication Management

Large amounts of data have been produced at a rapid rate since the invention of computers. This condition is the key motivation for up-to-date and forthcoming research frontiers. Replication is one of the mechanisms for managing data, since it improves data accessibility and reliability in the distributed database environment. In recent years, the amount of various data grows rapidly with widely available low-cost technology. Although we have been packed with data, we still have lacked of knowledge. Nevertheless, if the impractical data is used in database replication, this will cause waste of data storage and the time taken for a replication process will be delayed. This paper proposes Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) algorithm in order to handle fragmented database synchronous replication. BVAGQ-AR algorithm is capable for partitioning the database into disjoint fragments. Fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. Managing fragmented database replication becomes a concern for the administrator because the distributed database is disseminated into split replica partitions. The result from the experiment shows that handling fragmented database synchronous replication through proposed BVAGQ-AR algorithm able to preserve data consistency in distributed environment

IMPLEMENTATION ANALYSIS OF CUCKOO SEARCH FOR THE BENCHMARK ROSENBROCK AND LEVY TEST FUNCTIONS

This paper presents the implementation analysis of the benchmark Rosenbrock and Levy test functions using the Cuckoo Search with emphasis on the effect of the search population and iterations count in the algorithm’s search processes. After many experimental procedures, this study revealed that deploying a population of 10 nests is sufficient to obtain acceptable solutions to the Rosenbrock and Levy test functions (or any similar problem to these test landscapes). In fact, increasing the search population to 25 or more nests was a demerit to the Cuckoo Search as it resulted in increased processing overhead without any improvement in processing outcomes. In terms of the iteration count, it was discovered that the Cuckoo Search could obtain satisfactory results in as little as 100 iterations. The outcome of this study is beneficial to the research community as it helps in facilitating the choice of parameters whenever one is confronted with similar problems.

Comparative implementation of the benchmark Dejong 5 function using flower pollination algorithm and the African buffalo optimization

This communication presents experimental research findings on the application of the flower pollination algorithm (FPA) and the African buffalo optimization (ABO) to implement the complex and fairly popular benchmark Dejong 5 function. The study aims to unravel the untapped potential of FPA and the ABO in providing good solutions to optimization problems. In addition, it explores the Dejong 5 function with the hope of attracting the attention of the research community to evaluate the capacity of the two comparative algorithms as well as the Dejong 5 function. We conclude from this study that in implementing FPA and ABO for solving the benchmark Dejong 5 problem, a population of 10 search agents and using 1000 iterations can produce effective and efficient outcomes