Novel Artificial Human Optimization Field Algorithms - The Beginning

New Artificial Human Optimization (AHO) Field Algorithms can be created from scratch or by adding the concept of Artificial Humans into other existing Optimization Algorithms. Particle Swarm Optimization (PSO) has been very popular for solving complex optimization problems due to its simplicity. In this work, new Artificial Human Optimization Field Algorithms are created by modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO research articles based on Human Behavior, Human Cognition and Human Thinking etc. But there are no Hybrid PSO articles which based on concepts like Human Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field algorithms based on these research gaps. Some existing Hybrid PSO algorithms are given a new name in this work so that it will be easy for future AHO researchers to find these novel Artificial Human Optimization Field Algorithms. A total of 6 Artificial Human Optimization Field algorithms titled "Human Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)", "Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking Particle Swarm Optimization (HTPSO)" and "Human Disease Particle Swarm Optimization (HDPSO)" are tested by applying these novel algorithms on Ackley, Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results obtained are compared with PSO algorithm.Comment: 25 pages, 41 figure

Novel Artificial Human Optimization Field Algorithms – The Beginning

New Artificial Human Optimization (AHO) Field Algorithms can be created from scratch or by adding the concept of Artificial Humans into other existing Optimization Algorithms. Particle Swarm Optimization (PSO) has been very popular for solving complex optimization problems due to its simplicity. In this work, new Artificial Human Optimization Field Algorithms are created by modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO research articles based on Human Behavior, Human Cognition and Human Thinking etc. But there are no Hybrid PSO articles which are based on concepts like Human Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field algorithms based on these research gaps. Some existing Hybrid PSO algorithms are given a new name in this work so that it will be easy for future AHO researchers to find these novel Artificial Human Optimization Field Algorithms. A total of 6 Artificial Human Optimization Field algorithms titled "Human Safety Particle Swarm Optimization (HuSaPSO)", “Human Kindness Particle Swarm Optimization (HKPSO)", “Human Relaxation Particle Swarm Optimization (HRPSO)", “Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", “Human Thinking Particle Swarm Optimization (HTPSO)" and “Human Disease Particle Swarm Optimization (HDPSO)” are tested by applying these novel algorithms on Ackley, Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results obtained are compared with PSO algorithm

Artificial Excellence - A New Branch of Artificial Intelligence

"Artificial Excellence" is a new field which is invented in this article. Artificial Excellence is a new field which belongs to Artificial Human Optimization field. Artificial Human Optimization is a sub-field of Evolutionary Computing. Evolutionary Computing is a sub-field of Computational Intelligence. Computational Intelligence is an area of Artificial Intelligence. Hence after the publication of this article, "Artificial Excellence (AE)" will become popular as a new branch of Artificial Intelligence (AI). A new algorithm titled "Artificial Satish Gajawada and Durga Toshniwal Algorithm (ASGDTA)" is designed in this work. The definition of AE is given in this article followed by many opportunities in the new AE field. The Literature Review of Artificial Excellence field is shown after showing the definition of Artificial Intelligence. The new ASGDTA Algorithm is explained followed by Results and Conclusions

Nature Plus Plus Inspired Computing - The Superset of Nature Inspired Computing

The term Nature Plus Plus Inspired Computing is coined by us in this article The abbreviation for this new term is N IC Just like the C programming language is a superset of C programming language Nature Plus Plus Inspired Computing N IC field is a superset of the Nature Inspired Computing NIC field We defined and introduced Nature Plus Plus Inspired Computing Field in this work Several interesting opportunities in N IC Field are shown for Artificial Intelligence Field Scientists and Students We show a literature review of the N IC Field after showing the definition of Nature Inspired Computing NIC Field The primary purpose of publishing this innovative article is to show a new path to NIC Field Scientists so that they can come up with various innovative algorithms from scratch As the focus of this article is to introduce N IC to researchers across the globe we added N IC Field concepts to the Particle Swarm Optimization algorithm and created the Children Cycle Riding Algorithm CCR Algorithm Finally results obtained by CCR Algorithm are shown followed by Conclusion

Artificial Satisfaction - The Brother of Artificial Intelligence

John McCarthy (September 4, 1927 2013; October 24, 2011) was an American computer scientist and cognitive scientist. The term 201C;Artificial Intelligence201D; was coined by him (Wikipedia, 2020). Satish Gajawada (March 12, 1988 2013; Present) is an Indian Independent Inventor and Scientist. He coined the term 201C;Artificial Satisfaction201D; in this article (Gajawada, S., and Hassan Mustafa, 2019a). A new field titled 201C;Artificial Satisfaction201D; is introduced in this article. 201C;Artificial Satisfaction201D; will be referred to as 201C;The Brother of Artificial Intelligence201D; after the publication of this article. A new algorithm titled 201C;Artificial Satisfaction Algorithm (ASA)201D; is designed and implemented in this work. For the sake of simplicity, Particle Swarm Optimization (PSO) Algorithm is modified with Artificial Satisfaction Concepts to create the 201C;Artificial Satisfaction Algorithm (ASA).201D; PSO and ASA algorithms are applied on five benchmark functions. A comparision is made between the results obtained. The focus of this paper is more on defining and introducing 201C;Artificial Satisfaction Field201D; to the rest of the world rather than on implementing complex algorithms from scratch

Testing Multiple Strategy Human Optimization based Artificial Human Optimization Algorithms

Recently a new trend titled ‘Artificial Human Optimization’ has become popular in Evolutionary Computing Domain. More than 30 papers were published in this new field proposed in December 2016. ‘Hassan Satish Particle Swarm Optimization (HSPSO)’ and ‘Human Inspired Differential Evolution (HIDE)’ are the two latest Artificial Human Optimization algorithms proposed based on Multiple Strategy Human Optimization. In this paper we focus on Testing HSPSO and HIDE by applying these latest algorithms on Ackley, Bohachevsky, Booth, Three-Hump Camel and Beale benchmark functions. Results obtained for these Artificial Human Optimization Algorithms are compared with Differential Evolution and Particle Swarm Optimization

An Artificial Human Optimization Algorithm titled Human Thinking Particle Swarm Optimization

Artificial Human Optimization is a latest field proposed in December 2016. Just like artificial Chromosomes are agents for Genetic Algorithms, similarly artificial Humans are agents for Artificial Human Optimization Algorithms. Particle Swarm Optimization is very popular algorithm for solving complex optimization problems in various domains. In this paper, Human Thinking Particle Swarm Optimization (HTPSO) is proposed by applying the concept of thinking of Humans into Particle Swarm Optimization. The proposed HTPSO algorithm is tested by applying it on various benchmark functions. Results obtained by HTPSO algorithm are compared with Particle Swarm Optimization algorithm.  &nbsp