Computer Science & Engineering

Collection of Abstracts is a new kind of research paper where the complete focus is to show abstracts published in a particular field. Collection of Abstracts is generally published in conferences as pre-conference proceedings book and given to the authors on the day of conference. This kind of research papers give valuable information to researchers like “what are the titles? What are the abstracts? Who are the authors working in the field?”. The corresponding author of this paper published a paper titled “Entrepreneur : Artificial Human Optimization” where he collected 13 abstracts and shown them as it is in an attempt to propose a new field. This work was published in Transactions on Machine Learning and artificial Intelligence. Now, “Collection of Abstracts in Artificial Human Optimization Field” is the second research paper in the entire research industry, which is completely based on Abstracts of papers. The first 2 sections of this paper shows titles and abstracts of papers in Artificial Human Optimization Field. Third section shows corrections to previous work in Artificial Human Optimization Field.

Artificial Human Optimization is a new field that came into existence on December 2016. All the optimization algorithms that were created and are being created based on Artificial Humans will come under Artificial Human Optimization Field. Just like agents in Ant Colony Optimization are Artificial Ants, agents in Bee Colony Optimization are Artificial Bees, agents in Genetic Algorithms are Artificial chromosomes, agents in Particle Swarm Optimization are Artificial Birds or Artificial Fishes, similarly agents in Artificial Human Optimization Algorithms are Artificial Humans. “Multiple Strategy Human Optimization (MSHO)” is a new algorithm designed recently based on Artificial Humans. The key concept in MSHO is to use more than one strategy in the optimization process. Two strategies are used in MSHO. One strategy is to move towards the best individual in one generation. Another strategy is to move away from the worst individual in next generation. Differential Evolution is a popular algorithm for solving optimization problems in various domains. In this paper “Human Inspired Differential Evolution (HIDE)” is proposed. The idea of HIDE algorithm is to use the concept of Multiple Strategies of MSHO algorithm in Differential Evolution. The mutation operator of Differential Evolution algorithm is modified to incorporate the key concept of MSHO algorithm in Differential Evolution. The proposed HIDE algorithm is tested by applying it on a complex benchmark problem.

The main idea to author this article is to " Popularize Artificial Human Optimization Field like never before by showing an Overview of this new field ". This idea can be divided into following sub-ideas:
1) To show the definition of " Artificial Human Optimization Field (AHO Field) "
2) To show difficulty level of creating new algorithms under AHO field
3) To show 30+ titles of papers published under AHO field
4) To show names of 65+ authors who worked under AHO Field
5) To show best negative reviews obtained for work under AHO Field
6) To show best positive reviews obtained for work under AHO Field
7) To show feedback given by an expert for work under AHO Field
8) To show " Hassan Satish Particle Swarm Optimization (HSPSO) ". This is latest work under AHO Field
9) To show contribution of Satish Gajawada and co-authors to this new Field
10) To show surprising results obtained after implementing AHO algorithms
11) To show you " Future of Artificial Human Optimization Field "

This piece of research presents the Particle Swarm Optimization (PSO) as a biologically inspired computational paradigm searches for problem optimization technique. Specifically (PSO) consists of a swarm of particles, where particle represent a potential solution. More precisely it is a population-based, stochastic algorithm modeled on the social behaviors observed in flocking birds. Over the past quarter century, Global Optimization techniques like Genetic and PSO algorithms has attracted many researchers attention at engineering and industry. In December 2016, a new field titled “Artificial Human Optimization” was introduced in literature. Referring to that innovative field it is clear that the agents in Artificial Human Optimization are Artificial Humans. Recently, a new algorithm titled Multiple Strategy Human Optimization (MSHO) is designed based on Artificial Humans. This paper adopted an interesting novel experimental idea which incorporated Hybridization of perspective concepts of Artificial Human Optimization into some experimental illustrations of PSO algorithms. Additionally, Human Inspired Differential Evolution (HIDE) is recently proposed method which is based on Differential Evolution and MSHO. For particular parameters settings HIDE performed approximately as good as Differential Evolution. In the experiment in this paper, a new algorithm titled “Hassan Satish Particle Swarm Optimization (HSPSO)” is proposed. HSPSO is tested by applying it on a complex benchmark function. Interesting Hybridization results have been obtained. Results obtained by HSPSO are compared with Particle Swarm Optimization.

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term “Deep Loving”. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.

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 Conclusions.

Artificial Neural Networks Field (ANN Field) is an exciting field of research. ANN field took its inspiration from Human Brain. The heart and Brain are very important for the survival of Humans. Research Scientists published many articles by giving importance to Brain. But scientists have not yet explored much on the Heart which is another important part in addition to the Brain. The primary purpose of publishing this article is to show a path to ANN field Research Scientists by introducing the concept of “Heart” into Artificial Neural Networks. In this paper, we coined and defined “Artificial Heart Neuron,” which is the basic part of Artificial Heart Neural Networks Field (AHNN Field) in addition to Artificial
Neuron. This work takes its inspiration from both Heart and Brain.