The recently introduced Marine Predators Algorithm (MPA) exhibits competitive performance in solving optimization problems. However, it often prematurely converges due to an imbalance between its exploration and exploitation capabilities. Therefore, in this paper, an improved MPA variant using a proposed Local Escaping Operator (LEO) is introduced. In the MPA, solution candidates are replaced by better candidates from the previous iteration, mimicking a memory of already visited prey-abundant areas. This indicates a weak inter-dependence between the candidates in the population and possible acceptance of new solution candidates created outside the algorithm without damaging the optimization process. Consequently, in the proposed approach, the worst candidates are replaced with solutions created by the LEO. The LEO is based on representative solutions, taking into account the relationship between predators and the characteristics of their population. The approach is experimentally compared with the state-of-the-art meta-heuristics on 82 test functions, including IEEE Congress on Evolutionary Computation test suites (CEC’14 and CEC’17) and three engineering problems. The results show the superiority of the LEO-MPA over the MPA and recent algorithms. Furthermore, in this paper, the suitability of the hybridization of meta-heuristics with the LEO is discussed and successful attempts with the best algorithms are shown.

Enhanced Marine Predators Algorithm with Local Escaping Operator for Global Optimization

Marine Predators Algorithm (MPA) is the existing population-based meta-heuristic algorithms that falls under the category of Nature-Inspired Optimization Algorithm (NIOA) enthused by the foraging actions of the marine predators that principally pursues Levy or Brownian approach as its foraging strategy. Furthermore, it employs the optimal encounter rate stratagem involving both the predator as well as prey. Since its introduction by Faramarzi in the year 2020, MPA has gained enormous popularity and has been employed in numerous application areas ranging from Mathematical and Engineering Optimization problems to Fog Computing to Image Processing to Photovoltaic System to Wind-Solar Generation System for resolving continuous optimization problems. Such huge interest from the research fraternity or the massive recognition of MPA is due to several factors such as its simplicity, ease of application, realistic execution time, superior convergence acceleration rate, soaring effectiveness, its ability to unravel continuous, multi-objective and binary problems when compared with other renowned optimization algorithms existing in the literature. This paper offers a detailed summary of the Marine Predators Algorithm (MPA) and its variants. Furthermore, the applications of MPA in a number of spheres such as Image processing, classification, electrical power system, Photovoltaic models, structural damage detection, distribution networks, engineering applications, Task Scheduling, optimization problems etc., are illustrated. To conclude, the paper highlights and thereby advocates few of the potential future research directions for MPA. 

/pdf/an-inclusive-survey-on-marine-predators-algorithm-variants-2isgmzg8.pdf

An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Marine Predators Algorithm (MPA) is a recent nature-inspired optimizer stemmed from widespread foraging mechanisms based on Lévy and Brownian movements in ocean predators. Due to its superb features, such as derivative-free, parameter-less, easy-to-use, flexible, and simplicity, MPA is quickly evolved for a wide range of optimization problems in a short period. Therefore, its impressive characteristics inspire this review to analyze and discuss the primary MPA research studies established. In this review paper, the growth of the MPA is analyzed based on 102 research papers to show its powerful performance. The MPA inspirations and its theoretical concepts are also illustrated, focusing on its convergence behaviour. Thereafter, the MPA versions suggested improving the MPA behaviour on connecting the search space shape of real-world optimization problems are analyzed. A plethora and diverse optimization applications have been addressed, relying on MPA as the main solver, which is also described and organized. In addition, a critical discussion about the convergence behaviour and the main limitation of MPA is given. The review is end-up highlighting the main findings of this survey and suggests some possible MPA-related improvements and extensions that can be carried out in the future. 

/pdf/marine-predators-algorithm-a-review-1m72a7bk.pdf

Marine Predators Algorithm: A Review

In engineering design optimization problems, the optimal solution can improve the design quality of complex engineering system and reduce a lot of cost consumption, so it is of great practical significance to study the optimization algorithm of engineering design problems. Evolutionary computation is widely used to solve engineering design optimization problems, which are mostly mixed-integer nonlinear programming (MINLP) problems. As a newly developed evolutionary computing method, Marine Predator Algorithm (MPA) currently suffers from weak convergence and easily falls into local optimum. In order to overcome the disadvantage, this study proposed a Golden-Sine Dynamic Marine Predator Algorithm (GDMPA). Firstly, Logistic-Logistic (L-L) cascade chaos was used to adjust the initial position of the population to generate a high-quality initial prey population while ensuring ergodicity and randomness. Secondly, the dynamic adjustment transition probability strategy was added to improve the discriminant conditions when predators entered different stages, which effectively maintained the balance between global exploration and local exploitation. The adaptive inertial weight based on Sigmoid function was used in updating the step information of predators to avoid the problem of falling into local extrema. Finally, the Golden-Sine factor is employed to achieve a better balance between exploration and exploitation, further improve the premature convergence problem, enhance the population diversity, and improve the convergence rate. A series of validation studies were conducted over twelve standard test functions and the CEC2017 test set to verify the effectiveness and robustness of the improved GDMPA strategy. Mechanical optimization and size optimization study for truss structures was carried out using the proposed GDMPA, which yielded excellent results. The results of the 27-bar truss structure show that the proposed GDMPA reduces 3.24%, 27.18%, 39.38%, 27.65% and 9.67% compared to the total mass of MPA, BOA, SSA, SOA and HHO, respectively. In the other cases, the optimization results of GDMPA have been improved substantially compared with other algorithms. Therefore, GDMPA has a broad application prospect in structural design and optimization. 

Golden-Sine dynamic marine predator algorithm for addressing engineering design optimization

Marine predators algorithm (MPA) is a recently proposed metaheuristic algorithm that mimics the marine predators behavior when attacking their preys. Recently, the MPA has been broadly employed to tackle numerous optimization problems in various research areas and has confirmed its supremacy over a large number of the metaheuristic algorithms regard to convergence speed and accuracy thanks to its simplicity, flexible implementation and few adjustable parameters requirements. A comprehensive review of the MPA is presented in this paper along with its variants such as binary, discrete, modifications, hybridizations, chaotic, quantum and multi-objective versions. This paper also reviews various applications of MPA in electrical engineering, computer science, medicine, etc. Moreover, further research directions for MPA are suggested. The source code of the MPA can be found at: http://www.alimirjalili.com/MPA.html. 

Marine predators algorithm: A comprehensive review

A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

https://www.mdpi.com/2227-7390/9/3/291/pdf

A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

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A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM