A Modified Bio Inspired

Abstract

In this investigation, the authors intend to demonstrate the applicability of a recent spotlighted metaheuristic called the great salmon run (TGSR) algorithm for shape and size design of truss structures. The algorithmic functioning of TGSR emulates the annual migration of salmons together with dangers laid through their pathways. In a previous study by the authors, it has been proved that the method is as effective as most of the state-of-the-art metaheuristics for a wide range of numerical benchmark problems. Here, the authors utilize TGSR together with some rival metaheuristics, i.e. bee algorithm (BA), scale factor local search differential evolutionary algorithm (SFLSDEA), chaotic particle swarm optimization (CPSO) algorithm, self adaptive penalty function genetic algorithm (SAPFGA) and mutable smart bee algorithm (MSBA), for optimal design of truss structures with dynamic frequency constraints. To effectively handle the constraints, the authors take the advantage of self-adaptive penalty function (SAPF) constraint handling technique to free the user from any priori penalty coefficient tuning. Therefore, an algorithm for automation of constraint shape and size design of truss structures is proposed here. Furthermore, for more elaboration, the authors consider the results of some previous reports for same problems to find out whether TGSR is capable of yielding comparative results as compared to other metaheuristics. Through the experiments, the exploration/exploitation capabilities of TGSR for truss design are investigated. It is proved that TGSR is not only able to handle the nonlinearities and decision making difficulties associated with shape and size optimization of truss structures but also can show comparative results as compared to powerful state-of-the-art metaheuristics.

Integration of CFA 2 FB control scheme with modified shunt active line conditioner ( MSALC ) connected distribution system for mitigation of harmonics

“…It is a new meta-heuristic inspired by frivolous behavior in search of food. 56 Artificial feeding birds are simple, but efficient, to various optimization problems can be easily adapted. When searching for food a pigeon makes four types of moves: (1) Walking for new position near his current state (because they walk slowly), (2) In arbitrary semi-random position he flying and landing (3) returning and flying to a state of mind full of food (such as a picnic area), and (4) flying and landing near another pigeon.…”

“…Step 4: New Position Updation Using AFB Formulate the new solution using updating the pigeon walks for searching food (reflection and visibility of cuttlefish). 56,57 The initial population's maximum fitness and minimum fitness are determined. The best solutions are chosen from the minimum values of the solutions.…”

“…AFB is defined as Artificial Feeding Bird. It is a new meta‐heuristic inspired by frivolous behavior in search of food 56 . Artificial feeding birds are simple, but efficient, to various optimization problems can be easily adapted.…”

“…Formulate the new solution using updating the pigeon walks for searching food (reflection and visibility of cuttlefish) 56,57 . The initial population's maximum fitness and minimum fitness are determined.…”

Reactive Power Optimization Using New Enhanced Whale Optimization Algorithm

“…Attempts have been made to solve RPO problem using various classical methods such as linear programming, Newton method, and interior point based on stochastic and heuristics techniques (Rahman et al, 2015). Nonetheless, the methods have shown a number of inefficiency (Singh, 2018). Therefore, RPO becomes a suitable application to validate the robustness of a newly proposed swarm-intelligence algorithm.…”