Stochastic process and tutorial of the African buffalo optimization

Abstract

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 algorithms

Adaptive Test Suits Generation for Self-Adaptive Systems Using SPEA2 Algorithm

“…It is important to keep in mind that developing the tests and setting up the SAS are not simple tasks, and they may cost money. The authors want to extend their future work by utilizing the optimization strategies indicated in the research of other scholars [39][40][41][42][43][44][45][46][47]. Data Availability Statement: Data will be available upon reasonable request.…”

Optimal coordinated energy management in active distribution networks considering battery energy storage and price‐responsive demand

“…The coordinated scheduling of BESSs and MT unit for maximizing DPF is a complex non-linear multi-constraint optimization problem which can't be solved by employing conventional optimization techniques. Such complex combinatorial optimization problems can be solved by using AI-based natureinspired meta-heuristic or evolutionary techniques which may include genetic algorithm (GA) [40], gravitational search algorithm (GSA) [41,42], fireworks algorithm (FWA) [32], African buffalo optimization (ABO) [23,24] and many more. The ABO algorithm was first developed by Odili et al in 2015 [43].…”

“…For further details like basic steps, proposed modifications in standard variant, mathematical equations, and algorithm parameters' of MABO the readers may refer to refs. [23,24,43]. The structure of an individual buffalo is composed of decision variables of the optimization problem.…”

“…The DPF is maximized using a recently explored swarm intelligence‐based optimization technique namely modified African buffalo optimization (MABO). The MABO is proven to have high investigation, effectiveness and utilization competencies for these kind of engineering optimization problems [23, 24]. The salient contributions of the proposed methodology include: …”

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

“…Since thousands of executions can be adopted by search-based approaches to be effective and successful, this can be considered an achievement of the approach [41]. For future work, the authors want to extend their research using the optimization techniques mentioned in the research work of different researchers [60][61][62][63][64][65][66][67][68][69]. The concept should be examined in the future through different experiments with a considerably larger number of feature models, to compare them with those used in our proposed work.…”