Eurasian oystercatcher optimiser: New meta-heuristic algorithm

Abstract

Modern optimisation is increasingly relying on meta-heuristic methods. This study presents a new meta-heuristic optimisation algorithm called Eurasian oystercatcher optimiser (EOO). The EOO algorithm mimics food behaviour of Eurasian oystercatcher (EO) in searching for mussels. In EOO, each bird (solution) in the population acts as a search agent. The EO changes the candidate mussel according to the best solutions to finally eat the best mussel (optimal result). A balance must be achieved among the size, calories, and energy of mussels. The proposed algorithm is benchmarked on 58 test functions of three phases (unimodal, multimodal, and fixed-diminution multimodal) and compared with several important algorithms as follows: particle swarm optimiser, grey wolf optimiser, biogeography based optimisation, gravitational search algorithm, and artificial bee colony. Finally, the results of the test functions prove that the proposed algorithm is able to provide very competitive results in terms of improved exploration and exploitation balances and local optima avoidance.

An efficient and secure wearable sensor based remote healthcare monitoring system using adaptive dilated transformer Bi‐LSTM with gated recurrent unit

“…The suggested healthcare monitoring model is executed in Python software having a maximum iteration of 50, a population size of 10, and a chromosome length of 16, 50, and 4, respectively. The implemented model is contrasted with existing algorithms like “Dingo Optimizer (DO)‐AD‐Bi‐LSTM‐GRU, 31 Eurasian Oystercatcher Optimizer (EOO)‐AD‐Bi‐LSTM‐GRU, 32 Henry Gas Solubility Optimization (HGSO)‐AD‐Bi‐LSTM‐GRU, 33 HBO‐AD‐Bi‐LSTM‐GRU,” 27 and models like “LSTM, 25 Bi‐LSTM, 28 GRU, 29 and AD‐Bi‐LSTM‐GRU and with cryptography schemes like EHOBA‐Elliptical Curve Cryptography (ECC), 34 EHBOA‐Rivest, Shamir, Adleman (RSA), 35 EHBOA‐Data Encryption Standard (DES), 36 and EHBOA‐AES,” 26 respectively, to validate its performance.…”

“…Statistical report on the deployed disease classification model. 31 EOO-ADTBi-LSTM-GRU 32 HGSO-ADTBi-LSTM-GRU 33 HBO-ADTBi-LSTM-GRU 27…”

“…In Equation (31), the term ẽc represents the output candidate, tanh denotes the tanh activation function, A e represents the undetermined weight vector, and C e denotes the bias. The output vector from the GRU is given by Equation (32).…”

“…In Equation (32), the term e c represents the output vector. The pictorial representation of the GRU network is given in Figure 6.…”

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

“…( 2019 ) 186 Escaping Bird Search (EBS) Shahrouzi and Kaveh ( 2022 ) 187 Eurasian Oystercatcher Optimiser (EOO) Salim et al. ( 2022 ) 188 Evolution Strategies (ES) Beyer and Schwefel ( 2002 ) 189 Exchange Market Algorithm (EMA) Ghorbani et al. ( 2017 ) 190 Extremal Optimization (EO) Boettcher and Percus ( 1999 ) 191 Farmland Fertility Algorithm (FFA) Shayanfar and Gharehchopogh ( 2018 ) 192 Fast Bacterial Swarming Algorithm (FBSA) Chu et al.…”