Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency

SCSO: A novel sine-cosine based swarm optimization algorithm for numerical function optimization

“…These numerical benchmark functions which are listed in Table 1 are applied to widely used swarm optimization algorithms in order to obtain comparisons. Moth-flame optimization (MFO) [5] algorithm, artificial bee colony (ABC) algorithm [6], sine-cosine algorithm (SCA) [8], biogeography-based optimization (BBO) [12] and krill herd algorithm (KH) [15] and hybrid grey wolf optimizer sine cosine algorithm (HGWOSCA) [24] are used to obtain comparisons. Population, maximum iteration, dim of each object and other parameters of these algorithms are listed in Table 3.…”

“…In the PSO, social behavior of individuals of fish and bird swarms were mathematically modelled. Besides the PSO, many optimization algorithms such as artificial neural network [2,3], ant colony optimization (ACO) [4], moth-flame optimization (MFO) algorithm [5], artificial bee colony (ABC) algorithm [6] ,firefly algorithm [7], sine-cosine algorithm (SCA) [8], genetic algorithm (GA) [9], bat algorithm (BA) [10], differential evaluation (DE) [11], biogeography-based optimization (BBO) [12] , harmony search (HS) [13], gravitational search algorithm (GSA) [14], krill herd algorithm (KH) [15], etc were proposed in the literature. The main aim of these algorithms to find global optima value but some of them trapped local optima values.…”

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

“…In the STOC-ABC, Tent chaotic OBL initialization method is introduced to diversify the initial individuals and produce good initial solutions. G. Li et al (2014) presented an optimization technique based on ABC, where OBL is used in the population initialization, the greedy selection is excluded and the way that an employed bee transforms into a scout is modified. Lai and Qu (2012) proposed a modified strategy of initialization for the standard ABC, which utilizing the logistic map and OBL to generate the initial population as well as the scout bee position.…”

Artificial Intelligence Approach for Target Classification: A State of The Art

“…The choice of which method to use will then depend heavily on the application itself. It also prompts us to question the possibility of having a high-performance method at all levels and for all possible applications, including road safety [148][149][150][151][152]. The current trend is converging towards the development of new methods combining the properties of old algorithms and the expectations of new applications and adaptable to different uses.…”