A Novel Discrete Binary Gaining-Sharing knowledge-based Optimization Algorithm for the Travelling Counselling Problem for Utilization of Solar Energy

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Solar Energy ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knowledge Based ◽  
Power Stations ◽  
Binary Model ◽  
Application Problem ◽  
Counselling Process ◽  
Two Stages

This article proposes a novel binary version of recently developed Gaining-Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is proposed. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. Discrete Binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space.An improved scheduling of the technical counselling process for utilization of the electricity from solar energy power stations is introduced. The scheduling aims at achieving the best utilization of the available day time for the counselling group,n this regard,a new application problem is presented, which is called a Travelling Counselling Problem (TCP).A Nonlinear Binary Model is introduced with a real application

A Novel Discrete Binary Gaining-Sharing knowledge-based Optimization Algorithm for the Travelling Counselling Problem for Utilization of Solar Energy

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Solar Energy ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knowledge Based ◽  
Power Stations ◽  
Binary Model ◽  
Application Problem ◽  
Counselling Process ◽  
Two Stages

This article proposes a novel binary version of recently developed Gaining-Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is proposed. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. Discrete Binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space.An improved scheduling of the technical counselling process for utilization of the electricity from solar energy power stations is introduced. The scheduling aims at achieving the best utilization of the available day time for the counselling group,n this regard,a new application problem is presented, which is called a Travelling Counselling Problem (TCP).A Nonlinear Binary Model is introduced with a real application

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

scholarly journals Stochastic Travelling Advisor Problem Simulation with a Case Study: A Novel Binary Gaining-Sharing Knowledge-Based Optimization Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Said Ali Hassan ◽  
Yousra Mohamed Ayman ◽  
Khalid Alnowibet ◽  
Prachi Agrawal ◽  
Ali Wagdy Mohamed
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Health And Safety ◽  
Working Hours ◽  
Search Space ◽  
Advisory Group ◽  
Deterministic Models ◽  
Knowledge Based

This article proposes a new problem which is called the Stochastic Travelling Advisor Problem (STAP) in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since the deterministic models are not appropriate for such real-life problems. The STAP is handled by proposing suitable probability distributions for the time parameters and simulating the problem under such conditions. Many application problems like this one are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. A novel binary version of the recently developed gaining-sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is given. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. The binary version of GSK (BGSK) depends mainly on two stages that enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. The generated simulation runs of the example are solved using the BGSK, and the output histograms and the best-fitted distributions for the total profit and for the route length are obtained.

scholarly journals A Novel Multi-Objective Nonlinear Discrete Binary Gaining-Sharing knowledge-based Optimization Algorithm Optimum Scheduling of Flights for Residual S

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Mathematical Programming ◽  
Decision Maker ◽  
Optimization Algorithm ◽  
Programming Model ◽  
Search Space ◽  
Mathematical Programming Model ◽  
Practical Applications ◽  
Knowledge Based ◽  
Two Stages

GSK algorithm is based on the concept of how humans acquire and share knowledge through their lifespan. Discrete Binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Besides, one of these practical applications is to optimally schedule the flights for residual stranded citizens due to COVID-19. The problem is defined for a decision maker who wants to schedule a multiple stepped trip for a subset of candidate airports to return the maximum number of residuals of stranded citizens remaining in listed airports while comprising the minimization of the total travelled distances for a carrying airplane. A nonlinear binary mathematical programming model for the problem is introduced with a real application case study, the case study is solved using (DBGSK).

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals Modified Grasshopper Optimization Algorithm Based Genetic Algorithm for Global Optimization Problems: The System of Nonlinear Equations Case Study

2021 ◽  
Author(s):  
Hala A. Omar ◽  
Mohammed El-Shorbagy
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Local Minimum ◽  
Optimization Problems ◽  
Search Space ◽  
System Of Nonlinear Equations ◽  
Grasshopper Optimization Algorithm ◽  
Speed Up ◽  
Grasshopper Optimization

Abstract Grasshopper optimization algorithm (GOA) is one of the promising optimization algorithms for optimization problems. But, it has the main drawback of trapping into a local minimum, which causes slow convergence or inability to detect a solution. Several modifications and combinations have been proposed to overcome this problem. In this paper, a modified grasshopper optimization algorithm (MGOA) based genetic algorithm (GA) is proposed to overcome this problem. Modifications rely on certain mathematical assumptions and varying the domain of the Cmax control parameter to escape from the local minimum and move the search process to a new improved point. Parameter C is one of the most important parameters in GOA where it balances the exploration and exploitation of the search space. These modifications aim to lead to speed up the convergence rate by reducing the repeated solutions and the number of iterations. The proposed algorithm will be tested on the 19 main test functions to verify and investigate the influence of the proposed modifications. In addition, the algorithm will be applied to solve 5 different cases of nonlinear systems with different types of dimensions and regularity to show the reliability and efficiency of the proposed algorithm. Good results were achieved compared to the original GOA.

scholarly journals Lion optimization algorithm for team orienteering problem with time window

2021 ◽  
Vol 21 (1) ◽  
pp. 538
Author(s):  
Esam Taha Yassen ◽  
Alaa Abdulkhar Jihad ◽  
Sudad H. Abed
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Real Life ◽  
Search Space ◽  
Orienteering Problem ◽  
Team Orienteering Problem ◽  
Team Orienteering ◽  
Lion Optimization Algorithm

<span>Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate a trespass strong nomadic lion to prevail a pride by fighting its males. As a result of this improving process, an enhanced LA (ILA) emerged. The obtained solutions have been compared with the best known and standard results obtained in the former studies. The conducted experimental test verifies the effectiveness of the ILA in solving the TOPTW as it obtained a very competitive results compared to the LA and the state-of-the-art methods across all tested instances.</span>

scholarly journals Near Infrared and Visible Image Registration using Whale Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Image Registration ◽  
Optimization Algorithm ◽  
Near Infrared ◽  
Optimization Problems ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Visible Image ◽  
Local Optima ◽  
Multimodal Image Registration ◽  
Whale Optimization

This paper reports the use of a nature-inspired metaheuristic algorithm known as ‘Whale Optimization Algorithm’ (WOA) for multimodal image registration. WOA is based on the hunting behaviour of Humpback whales and provides better exploration and exploitation of the search space with small possibility of trapping in local optima. Though WOA is used in various optimization problems, no detailed study is available for its use in image registration. For this study different sets of NIR and visible images are considered. The registration results are compared with the other state of the art image registration methods. The results show that WOA is a very competitive algorithm for NIR-visible image registration. With the advantages of better exploration of search space and local optima avoidance, the algorithm can be a suitable choice for multimodal image registration.

scholarly journals Fertilization optimization algorithm on CEC2015 and large scale problems

2021 ◽  
Author(s):  
Hazim Nasir Ghafil ◽  
Shaymaa Alsamia ◽  
Károly Jármai
Keyword(s):  
Optimization Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Random Search ◽  
Search Space ◽  
Large Scale Optimization ◽  
Biologically Inspired ◽  
Large Scale Problems ◽  
Comparison Results ◽  
Scale Optimization

Abstract This work, presents a novel optimizer called fertilization optimization algorithm, which is based on levy flight and random search within a search space. It is a biologically inspired algorithm by the fertilization of the egg in reproduction of mammals. The performance of the algorithm was compared with other optimization algorithms on CEC2015 time expensive benchmarks and large scale optimization problems. Remarkably, the fertilization optimization algorithm has overcome other optimizers in many cases and the examination and comparison results are encouraging to use the fertilization optimization algorithm in other possible applications.

scholarly journals Bacterial foraging optimization algorithm with mutation to solve constrained problems

2019 ◽  
Vol 29 ◽  
pp. 1-16
Author(s):  
Betania Hernández-Ocaña ◽  
José Hernández-Torruco ◽  
Oscar Chávez-Bosquez ◽  
Juana Canul-Reich ◽  
Luis Gerardo Montané-Jiménez
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Mutation Operator ◽  
Bacterial Foraging Optimization ◽  
Previous Version ◽  
Bacterial Foraging ◽  
Constrained Problems ◽  
Swarm Intelligence Algorithm ◽  
Bacterial Foraging Optimization Algorithm

A simple version of a Swarm Intelligence algorithm called bacterial foraging optimization algorithm with mutation and dynamic stepsize (BFOAM-DS) is proposed. The bacterial foraging algorithm has the ability to explore and exploit the search space through its chemotactic operator. However, premature convergence is a disadvantage. This proposal uses a mutation operator in a swim, similar to evolutionary algorithms, combined with a dynamic stepsize operator to improve its performance and allows a better balance between the exploration and exploitation of the search space. BFOAM-DS was tested in three well-known engineering design optimization problems. Results were analyzed with basic statistics and common measures for nature-inspired constrained optimization problems to evaluate the behavior of the swim with a mutation operator and the dynamic stepsize operator. Results were compared against a previous version of the proposed algorithm to conclude that BFOAM-DS is competitive and better than a previous version of the algorithm.

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