A multigroup marine predator algorithm and its application for the power system economic load dispatch

Abstract

Marine Predator Algorithm (MPA) is an optimization algorithm inspired by the behavior of predator and prey to catch their own food. MPA is simple and easy to implement. To further improve the performance of MPA, this paper proposes a Multigroup Marine Predator Algorithm (MGMPA). The multigroup mechanism is to divide the initial population into several independent groups. These groups generate the top predator and the Elite matrix based on different strategies and share information after a fixed iteration. Above strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. To verify its performance, the paper compares MGMPA with some classic algorithms such as Particle Swarm Optimization (PSO), Parallel Particle Swarm Optimization (PPSO), Slap Swarm Algorithm (SSA), and Marine Predator Algorithm (MPA). In addition, the proposed MGMPA is also applied to solve Economic Load Dispatch problem (ELD). The experimental results show that the proposed MGMPA has significant advantages under the CEC2013 suite and obtains the minimum cost of power system operation and the maximum economic benefits in the application.

Elimination of Harmonics in Multilevel Inverter Using Multi-Group Marine Predator Algorithm-Based Enhanced RNN

“…is research presents an MGMPA [43] to increase the performance of MPA. e multigroup mechanism divides the original population into a number of distinct groups.…”

A Hybrid Capuchin Search Algorithm with Gradient Search Algorithm for Economic Dispatch Problem

“…The majority of MH methods applied in solving ELD problems are either modified versions or hybrids. Such modified MH algorithms include island-based harmony search algorithm [10], evolutionary simplex adaptive Hooke-Jeeves algorithm [11], multi-group marine predator algorithm [12], modified Krill Herd Algorithm (KHA) [13], and many more. Such hybrid MH algorithms include hybrid salp swarm algorithm [14], hybrid Grey Wolf Op-timizer (GWO) [15], and others.…”

“…In general, there are two main strategies for dealing with the constraints in an ELD solution: penalty or feasibility [9]. In the penalty strategy, the violations of the constraints may occur in the solution and the significant penalty weight of each constraint violated will be embedded in the evaluation function [12] or an independent objective function can be drafted to handle the violated constraints [14]. The feasibility strategy precludes any constraint violation in the solution.…”

Economic dispatch using modified bat algorithm

“…In recent years, with the rapid development of intelligent optimization algorithms, more and more scholars are using it to solve ED problems (Niu et al, 2014;Dou and Qin, 2020). grey wolf optimization (GWO) (Singh and Dhillon, 2019), sailfish algorithm (SFA) (Li et al, 2021), fireworks algorithm (FWA) (Zare et al, 2021), whale optimization algorithm (WOA) (Medani et al, 2018), artificial bee colony algorithm (ABC) (Hassan et al, 2020), ant colony optimization (ACO) (Zhou et al, 2017), social spider algorithm (SSA) (Adhvaryyu and Adhvaryyu, 2020), marine predator algorithm (MPA) (Pan et al, 2021), ant lion optimizer (ALO) (Mouassa et al, 2017), bat algorithm (BA) (Rugema et al, 2021), and other optimization algorithms have been applied to the solution of ED problems. Ref.…”