A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm

Ghasemi-Marzbali, Ali

doi:10.1007/s00500-020-04721-1

Cited by 45 publications

(15 citation statements)

References 35 publications

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“…The intelligent behavior of the individual search agent in the swarm and the combination of these behaviors offer these kinds an advantage over other algorithms in achieving the global optima. The most widely used swarm algorithms are as follows: particle swarm optimization (PSO), 19 mothflame optimization algorithm (MFO), 20 social spider optimization (SSO), 21 grey wolf optimizer (GWO), 22 artificial bee colony (ABC), 23 grasshoppers optimization algorithm (GOA), 24 border collie optimization (BCO), 11 whale optimization algorithm (WOA), 13 salp swarm algorithm (SSA), 25 Harris hawk optimization (HHO), 26 bear smell search algorithm BSSA, 27 bonobo optimizer (BO), 28 moth search algorithm (MSA), 29 hunger games search (HGS), 30 colony predation algorithm (CPA), 31 and monarch butterfly optimization (MBO). 32 Physical-based algorithms exploit the occurrence of physical phenomena to execute the optimization paradigm, as follows: atom search optimization (ASO), 33 simulated annealings (SA), 34 water cycle algorithm (WCA), 35 gravitational search optimization algorithm (GSA), 36 water evaporation optimization (WEO), 37 lightning search algorithm (LSA), 38 equilibrium Optimizer (EO), 39 and artificial ecosystem-based optimization (AEO), 10 and slime mold algorithm (SMA).…”

Section: Overview Of the Optimization Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal allocation of distributed generation with the presence of photovoltaic and battery energy storage system using improved barnacles mating optimizer

Selim

Kamel

Zawbaa

et al. 2022

Energy Science & Engineering

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This paper proposes an improved version of Barnacles mating optimizer (BMO) for solving the optimal allocation problem of distribution generator (DGs) in radial distribution systems (RDSs). BMO is a recent bioinspired optimization algorithm that mimics the intelligence behavior of Barnacles' mating. However, like with any metaheuristic optimization approach, it may face issues such as local optima trapping and low convergence rate. Hence, an improved BMO is adopted based on the quasi oppositional (QOBMO) and the chaos maps theories (CQOBMO). The two improvement methods are applied to increase the convergence performance of the conventional BMO. To prove the efficiency of the improved QOBMO and CQOBMO algorithms, 23 benchmark functions are used, and the accomplished results are compared with the conventional BMO. Then, the improved algorithm is applied to minimize the total power and energy losses in the distribution systems considering the uncertainty of DG power generation and time-varying load demand. The uncertainty of DG is represented using photovoltaic-based DG (PVDG). The improved method is employed to find the optimal power scheduling of PVDG and battery energy storage (BES) during 24 h. Two standard IEEE RDS (IEEE 33-bus and IEEE 69-bus) are used to simulate the case studies. Finally, the obtained results show that significant loss reductions (LRs) are achieved using the improved BMO where LRs reach 65.26%, and 68.86% in IEEE 33-bus and 69-bus, respectively, in the case of PVDG integration. However, using PVDG and BES the energy loss reductions reach 64% and 67.80% in IEEE 33-bus and 69-bus, respectively, which prove the efficiency of the improved BMO algorithm in finding the optimal solutions obtained so far.

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Section: Overview Of the Optimization Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal allocation of distributed generation with the presence of photovoltaic and battery energy storage system using improved barnacles mating optimizer

Selim

Kamel

Zawbaa

et al. 2022

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…The proposed hierarchy system of GWO provides diversity that can help to achieve good results. In [25], a novel optimization algorithm called Equilibrium Optimizer (EO) is Henry's law 2019 Harris hawks optimization (HHO) [27] Harris hawks attacking strategies 2019 Atom search optimization [28] Atomic motion model 2019 Pathfinder algorithm [29] Collective movements of swarms 2019 Sailfish Optimizer [30] Sailfish group hunting 2019 Equilibrium optimizer [25] Mass balance for a control volume 2020 Marine Predators Algorithm [31] foraging strategy of ocean predators 2020 Heap-based optimizer [32] Corporate rank hierarchy 2020 Gradient-based optimizer [33] Gradient-based Newton's approach 2020 Mayfly optimization algorithm [34] Flight behaviour of mayflies 2020 Bear smell search algorithm [35] Smelling mechanism of bears 2020 Political Optimizer [36] Multi-phased political process 2020 Group teaching optimization algorithm [37] Group teaching mechanism 2020 The Arithmetic Optimization Algorithm [38] Arithmetic operators 2021 Archimedes optimization algorithm [39] Archimedes' principle 2021 Aquila Optimizer [40] Aquila's behaviors 2021 Red fox optimization algorithm [41] Red fox hunting 2021 Horse herd optimization algorithm [42] Horses' herding behavior 2021 Remora optimization algorithm [43] Parasitic behavior of remora 2021 Dwarf Mongoose Optimization Algorithm [44] Foraging behavior of the dwarf mongoose 2022 Snake Optimizer [45] Mating behavior of snakes 2022 Ebola Optimization Algorithm [46] Propagation of the Ebola virus 2022 Reptile Search Algorithm [47] Hunting behaviour of Reptiles 2022…”

Section: Literature Reviewmentioning

confidence: 99%

Single candidate optimizer: a novel optimization algorithm

et al. 2022

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Single-solution-based optimization algorithms have gained little to no attention by the research community, unlike population-based approaches. This paper proposes a novel optimization algorithm, called Single Candidate Optimizer (SCO), that relies only on a single candidate solution throughout the whole optimization process. The proposed algorithm implements a unique set of equations to effectively update the position of the candidate solution. To balance exploration and exploitation, SCO is integrated with the two-phase strategy where the candidate solution updates its position differently in each phase. The effectiveness of the proposed approach is validated by testing it on thirty three classical benchmarking functions and four real-world engineering problems. SCO is compared with three well-known optimization algorithms, i.e., Particle Swarm Optimization, Grey Wolf Optimizer, and Gravitational Search Algorithm and with four recent high-performance algorithms: Equilibrium Optimizer, Archimedes Optimization Algorithm, Mayfly Algorithm, and Salp Swarm Algorithm. According to Friedman and Wilcoxon rank-sum tests, SCO can significantly outperform all other algorithms for the majority of the investigated problems. The results achieved by SCO motivates the design and development of new single-solution-based optimization algorithms to further improve the performance. The source code of SCO is publicly available at: https://uk.mathworks.com/matlabcentral/fileexchange/116100-single-candidate-optimizer.

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“…The results showed the superiority of the proposed algorithm. Then, Ghasemi-Marzbali [31] presented a novel nature-inspired meta-heuristic optimization; bear smell search algorithm (BSSA) that took into account powerful global and local search operators. Neetesh [32] demonstrated the dynamic foraging behavior of Agama lizards and built a mathematical model to simulate their foraging methods as an the artificial lizard search optimization (ALSO) algorithm.…”

Section: Heuristic Optimizationmentioning

confidence: 99%

Heuristic EPnP-Based Pose Estimation for Underground Machine Tracking

Zheng

Song

et al. 2022

Symmetry

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Pose estimation is one of the most complicated and compromising problems for underground mining machine tracking, and it is particularly important for hydraulic support autonomous following mining machine (AFM) policy-making system. In this paper, a low-cost infrared vision-based system through an Efficient Perspective-n-Point (EPnP) algorithm is proposed. To improve efficiency and simplify computation, a traditional EPnP algorithm is modified through a nature-inspired heuristic optimization algorithm. The optimized algorithm is integrated into the AFM policy-making system to estimate the relative pose (R-Pose) estimation between hydraulic support and the mining machine’s shearer drum. Simple yet effective numerical simulations and industrial experiments were carried out to validate the proposed method. The pose estimation error was ≤1% under normal lighting and illuminance, and ≤2% in a simulated underground environment, which was accurate enough to meet the needs of practical applications. Both numerical simulation and industrial experiment proved the superiority of the approach.

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A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm

Cited by 45 publications

References 35 publications

Optimal allocation of distributed generation with the presence of photovoltaic and battery energy storage system using improved barnacles mating optimizer

Optimal allocation of distributed generation with the presence of photovoltaic and battery energy storage system using improved barnacles mating optimizer

Single candidate optimizer: a novel optimization algorithm

Heuristic EPnP-Based Pose Estimation for Underground Machine Tracking

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