Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations

Abstract

Meta-heuristic algorithms have been effectively employed to tackle a wide range of optimisation issues, including structural engineering challenges. The optimisation of the shape and size of large-scale truss structures is difficult due to the nonlinear interplay between the cross-sectional and nodal coordinate pressures of structures. Recently, it was demonstrated that the newly proposed Marine Predator Algorithm (MPA) performs very well on mathematical challenges. The MPA is a meta-heuristic that simulates the essential hunting habits of natural marine predators. However, this algorithm has some disadvantages, such as becoming locked in locally optimal solutions and not exhibiting a high level of exploratory behaviour. This paper proposes two hybrid marine predator algorithms, Nonlinear Marine Predator (HNMPA) and Nonlinear-Chaotic Marine Predator Algorithm (HNCMPA), as improved variations of the marine predator algorithm paired with a hill-climbing (HC) technique for truss optimisation on form and size. The major advantage of these techniques are that they seek to overcome the MPA's disadvantages by using nonlinear values and prolonging the exploration phase with chaotic values; also, the HC algorithm has been used to avoid locally optimum solutions. In terms of truss optimisation performance, the proposed algorithm is compared to fourteen well-known meta-heuristics, including the Dragonfly Algorithm (DA), Henry Gas Solubility optimisation (HGSO), Arithmetic optimisation Algorithm (AOA), Generalized Normal Distribution Optimisation (GNDO), Salp Swarm Algorithm (SSA), Marine Predators Algorithm (MPA), Neural Network Algorithm (NNA), Water Cycle Algorithm (WCA), Artificial Gorilla Troops Optimiser (GTO), Gray Wolf Optimiser (GWO), Moth Flame Optimiser (MFO), Multi-Verse Optimiser (MVO), Equilibrium Optimiser (EO), and Cheetah Optimiser (CO). Furthermore, seven algorithms were chosen to test HNCMPA performance on benchmark optimisation sets, including MPA, MVO, PSO, MFO, SSA, GWO, and WOA. The results of the experiment demonstrate that the optimisation techniques put forth surpass previously established meta-heuristics in the field of optimisation, encompassing both traditional and CEC problems, by a margin of over 95% in terms of attaining a superior ultimate solution. Additionally, with regards to solving truss optimisation difficulties as a large-scale real-world engineering challenge, the outcomes indicate a performance boost of over 65% in obtaining significantly better solutions for problems involving 260-bar and 314-bar; conversely, in the case of 340-bar issues, the improvement rate is slightly lower at almost 25%.

A Multi-Objective Improved Hybrid Butterfly Artificial Gorilla Troop Optimizer for Node Localization in Wireless Sensor Groundwater Monitoring Networks

“…Figure 6 shows a comparison of various algorithms in terms of average error. At the beginning iterations (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), the ARO and HGS algorithms have higher average errors than the others, whereas the proposed iHBAGTO algorithm has the lowest average error. As the number of iterations increases, the average error decreases for all algorithms, but the iHBAGTO algorithm consistently outperforms the others with the lowest average error.…”

“…By examining the convergence rates of these algorithms, the effectiveness and efficiency of each algorithm in solving optimization problems can be assessed. The iHBAGTO algorithm's performance was evaluated alongside those of these established algorithms to provide insights into the algorithm's comparative strengths and weaknesses [14].…”

“…NGO follows this with a convergence rate of 70.3% at 10 iterations, which improves up to 94.5% at 100 iterations. to provide insights into the algorithm's comparative strengths and weaknesses [14]. Figure 5 displays the convergence rates of five optimization algorithms.…”

“…Resulting hybrid algorithms, such as the iHBAGTO algorithm, have been shown to outperform other localization algorithms in terms of accuracy and convergence rate. In developing a new node localization model, the performances of wireless sensor networks (WSNs) should be improved [14]. The localization model should be able to accurately determine the locations of sensor nodes in dense networks to enable the efficient placement of WSNs.…”

Task Allocation of Heterogeneous Multi-Unmanned Systems Based on Improved Sheep Flock Optimization Algorithm

“…Faramarzi [24] proposed a marine predator algorithm that integrates multiple mechanisms to achieve improved global search capabilities, but a specific approach for addressing local optima is lacking. Dehkordi [25] combined the marine predator algorithm with the hill climbing algorithm; when trapped in local optima for an extended period, the hill climbing algorithm, which is based on a greedy strategy, is used to attempt an escape, but the probability of escaping local optima with this method is relatively low.…”