In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions ...In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the haircut.These processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for optimization.The performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test suite.This comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark functions.To validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable efficiency.The results confirm BaOA’s versatility and reliability as an optimization tool.This study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.展开更多
Metaheuristic algorithms are widely used in solving optimization problems.In this paper,a new metaheuristic algorithm called Skill Optimization Algorithm(SOA)is proposed to solve optimization problems.The fundamental ...Metaheuristic algorithms are widely used in solving optimization problems.In this paper,a new metaheuristic algorithm called Skill Optimization Algorithm(SOA)is proposed to solve optimization problems.The fundamental inspiration in designing SOA is human efforts to acquire and improve skills.Various stages of SOA are mathematically modeled in two phases,including:(i)exploration,skill acquisition from experts and(ii)exploitation,skill improvement based on practice and individual effort.The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal,high-dimensional multimodal,and fixed-dimensional multimodal types.The optimization results show that SOA,by balancing exploration and exploitation,is able to provide good performance and appropriate solutions for optimization problems.In addition,the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed approach.Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achievesmuch more competitive results.展开更多
This paper introduces a newmetaheuristic algorithmcalledMigration Algorithm(MA),which is helpful in solving optimization problems.The fundamental inspiration of MA is the process of human migration,which aims to impro...This paper introduces a newmetaheuristic algorithmcalledMigration Algorithm(MA),which is helpful in solving optimization problems.The fundamental inspiration of MA is the process of human migration,which aims to improve job,educational,economic,and living conditions,and so on.Themathematicalmodeling of the proposed MAis presented in two phases to empower the proposed approach in exploration and exploitation during the search process.In the exploration phase,the algorithm population is updated based on the simulation of choosing the migration destination among the available options.In the exploitation phase,the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions.MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite.In addition,MA’s results are compared with the performance of twelve well-known metaheuristic algorithms.The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems.The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions.Also,the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.展开更多
Anthropogenic activities have contributed to pollution of water bodies through deposition of diverse pollutants amongst which are heavy metals. These pollutants, which at times are above the maximum concentration leve...Anthropogenic activities have contributed to pollution of water bodies through deposition of diverse pollutants amongst which are heavy metals. These pollutants, which at times are above the maximum concentration levels recommended, are detrimental to the quality of the water, soil and crops (plant) with subsequent human health risks. The objective of the work was to evaluate the impacts of human-based activities on the heavy metal properties of surface water with focus on the Kumba River basin. Field observations, interviews, field measurements and laboratory analyses of different water samples enabled us to collect the different data. The results show four main human-based activities within the river basin (agriculture, livestock production, domestic waste disposal and carwash activities) that pollute surface water. Approximately 20.61 tons of nitrogen and phosphorus from agricultural activities, 156.48 tons of animal wastes, 2517.5 tons of domestic wastes and 1.52 tons of detergent from carwash activities were deposited into the river each year. A highly significant difference at 1% was observed between the upstream and downstream heavy metal loads in four of the five heavy metals tested except for copper that was not significant. Lead concentrations were highest in all the activities with an average of 2.4 mg∙L<sup>−</sup><sup>1</sup> representing 57.81%, followed by zinc with 1.596 mg∙L<sup>−</sup><sup>1</sup> (38.45%) and manganese with 0.155 mg∙L<sup>−</sup><sup>1</sup> (3.74%) for the different anthropogenic activities thus indicating that these activities highly lead to pollution of the Kumba River water. The level of zinc and manganese was significantly influenced at ρ 005 by anthropogenic activities though generally the variations were in the order: carwash (3.196 mg∙L<sup>−</sup><sup>1</sup>) < domestic waste disposal (3.347 mg∙L<sup>−</sup><sup>1</sup>) < agriculture (4.172 mg∙L<sup>−</sup><sup>1</sup>) < livestock (4.886 mg∙L<sup>−</sup><sup>1</sup>) respectively and leading to a total of 14.04 tons of heavy metal pollutants deposited each day.展开更多
文摘In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the haircut.These processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for optimization.The performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test suite.This comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark functions.To validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable efficiency.The results confirm BaOA’s versatility and reliability as an optimization tool.This study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.
基金supported by Specific Research project 2022 Faculty of Education,University of Hradec Kralove.
文摘Metaheuristic algorithms are widely used in solving optimization problems.In this paper,a new metaheuristic algorithm called Skill Optimization Algorithm(SOA)is proposed to solve optimization problems.The fundamental inspiration in designing SOA is human efforts to acquire and improve skills.Various stages of SOA are mathematically modeled in two phases,including:(i)exploration,skill acquisition from experts and(ii)exploitation,skill improvement based on practice and individual effort.The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal,high-dimensional multimodal,and fixed-dimensional multimodal types.The optimization results show that SOA,by balancing exploration and exploitation,is able to provide good performance and appropriate solutions for optimization problems.In addition,the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed approach.Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achievesmuch more competitive results.
基金supported by the Project of Excellence PˇrFUHKNo.2210/2023-2024,University of Hradec Kralove,Czech Republic.
文摘This paper introduces a newmetaheuristic algorithmcalledMigration Algorithm(MA),which is helpful in solving optimization problems.The fundamental inspiration of MA is the process of human migration,which aims to improve job,educational,economic,and living conditions,and so on.Themathematicalmodeling of the proposed MAis presented in two phases to empower the proposed approach in exploration and exploitation during the search process.In the exploration phase,the algorithm population is updated based on the simulation of choosing the migration destination among the available options.In the exploitation phase,the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions.MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite.In addition,MA’s results are compared with the performance of twelve well-known metaheuristic algorithms.The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems.The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions.Also,the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.
文摘Anthropogenic activities have contributed to pollution of water bodies through deposition of diverse pollutants amongst which are heavy metals. These pollutants, which at times are above the maximum concentration levels recommended, are detrimental to the quality of the water, soil and crops (plant) with subsequent human health risks. The objective of the work was to evaluate the impacts of human-based activities on the heavy metal properties of surface water with focus on the Kumba River basin. Field observations, interviews, field measurements and laboratory analyses of different water samples enabled us to collect the different data. The results show four main human-based activities within the river basin (agriculture, livestock production, domestic waste disposal and carwash activities) that pollute surface water. Approximately 20.61 tons of nitrogen and phosphorus from agricultural activities, 156.48 tons of animal wastes, 2517.5 tons of domestic wastes and 1.52 tons of detergent from carwash activities were deposited into the river each year. A highly significant difference at 1% was observed between the upstream and downstream heavy metal loads in four of the five heavy metals tested except for copper that was not significant. Lead concentrations were highest in all the activities with an average of 2.4 mg∙L<sup>−</sup><sup>1</sup> representing 57.81%, followed by zinc with 1.596 mg∙L<sup>−</sup><sup>1</sup> (38.45%) and manganese with 0.155 mg∙L<sup>−</sup><sup>1</sup> (3.74%) for the different anthropogenic activities thus indicating that these activities highly lead to pollution of the Kumba River water. The level of zinc and manganese was significantly influenced at ρ 005 by anthropogenic activities though generally the variations were in the order: carwash (3.196 mg∙L<sup>−</sup><sup>1</sup>) < domestic waste disposal (3.347 mg∙L<sup>−</sup><sup>1</sup>) < agriculture (4.172 mg∙L<sup>−</sup><sup>1</sup>) < livestock (4.886 mg∙L<sup>−</sup><sup>1</sup>) respectively and leading to a total of 14.04 tons of heavy metal pollutants deposited each day.
