Julius Beneoluchi Odili

Solving the Traveling Salesman's Problem using the African Buffalo Optimization

This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic algorithm that is derived from careful observation of the African buffalos, a species of wild cows, in the African forests and savannahs. This animal displays uncommon intelligence, strategic organizational skills, and exceptional navigational ingenuity in its traversal of the African landscape in search for food. The African Buffalo Optimization builds a mathematical model from the behavior of this animal and uses the model to solve 33 benchmark symmetric Traveling Salesmans Problem and six difficult asymmetric instances from the TSPLIB. This study shows that buffalos are able to ensure excellent exploration and exploitation of the search space through regular communication, cooperation, and good memory of its previous personal exploits as well as tapping from the herds collective exploits. The results obtained by using the ABO to solve these TSP cases were benchmarked against the results obtained by using other popular algorithms. The results obtained using the African Buffalo Optimization algorithm are very competitive.

Response option for attacks detected by intrusion detection system

In past decades, we have seen that the increasing speed of the network attacks compromising computer system functionality and degrading network performance. The security of these systems has attracted a lot of research in the field of intrusion detection and response system to reduce the effect of these attacks. Response is a major part of intrusion detection system. Intrusion detection system without a timely response is not considered good even they detect threat and generate alarms. Optimum response is based on the selection of proper response option. In this paper, we categorize the attacks and propose some response option to thwart these attacks.

A comparative study of African Buffalo Optimization and Randomized Insertion Algorithm for asymmetric Travelling Salesman's Problem

In this study, a comparative study of the African Buffalo Optimization algorithm and the Randomized Insertion Algorithm to solving the asymmetric Travelling Salesmans Problem is made with the aim of ascertaining a better method to solving the asymmetric Travelling Salesmans Problem instances. The choice of the Random Insertion Algorithm as a comparative algorithm was informed by the fact that it has the best results in literature. The Randomized Insertion and African Buffalo Optimization algorithms employ two different methods in attempting solutions to ATSP: the African Buffalo Optimization employs the modified Karp-Steele approach while the Randomized Insertion uses random insertion approach. After attempting 15 benchmark ATSP cases out of the 19 datasets available in TSPLIB, it was discovered that the African Buffalo Optimization achieves slightly better result to the problems and at a much faster speed.

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system’s gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

African Buffalo Optimization Algorithm for Collision-avoidance in Electric Fish

This paper presents the African Buffalo Optimization algorithm for collision avoidance among electric fishes. Collision-avoidance in electric fish finds correlation with the Travelling Salesman avoiding the cities he has earlier visited. Collision avoidance in electric is akin to collision-avoidance in modern day driverless cars being promoted by Google Incorporation and other similar companies. The concept of collision-avoidance is also very useful to persons with visual impairment as it will help them avoid collision with objects, vehicles, persons, especially other visually-impaired. After a number of experimental procedures using the concept of the travelling salesman’s problem to simulate collision-avoidance in electric fish, this study concludes that the African Buffalo Optimization is a veritable tool for simulating collision avoidance in electric fishes.

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 algorithms 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 Comparative Evaluation of Swarm Intelligence Techniques for Solving Combinatorial Optimization Problems

This article presents a critical evaluation of swarm intelligence techniques for solving combinatorial optimization problems. Since, unarguably, the traveling salesman’s problem is the most developed, studied, and popular combinatorial problem, this study uses it as a benchmark. After a number of experimental investigations involving 24 popular but complex benchmark symmetric traveling salesman’s problem instances and 15 asymmetric traveling salesman’s problem of the 19 instances available in TSPLIB95, the African buffalo optimization proved to be the best algorithm in terms of efficiency and effectiveness in solving the problems under investigation.

Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management

This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. The study reveals that the African Buffalo Optimization and the Ant Colony Optimization are the best in solving the symmetric TSP, which is similar to intelligence gathering channel in the strategic management of big organizations, while the Randomized Insertion Algorithm holds the best promise in asymmetric TSP instances akin to strategic information exchange channels in strategic management.