Problem Solving, Particle Swarm Optimization, & Lewis Harrison’s Applied Game Theory

I was having a conversation with one of my students this week about how mathematics and computer sciences can solve extremely complex problems. She specifically asked me to describe it in a simple and understandable way.

This was not difficult to do since though I have some skill at understanding complex theories related to problem solving and decision science but I’m not very skilled at mathematics.

I walked with her outside of our retreat center and pointed to the sky. There was a swarm of Canadian Geese flying over head. I said to her, “That is a Particle Swarm. That pattern of geese is the basis of one of the most important problem solving t- tools ever developed “Particle Swarm Optimization”.

PSO. among the most common metaheuristic algorithms is  a computational method often used in In computer science,  that optimizes a problem by trying to improve a   a member of a set of possible solutions(a candidate solution) to a given problem with regard to a given measure of quality. Described in more simple terms Particle Swarm Optimization (PSO)  is a mathematical, population  based algorithm for solving problems related to social behavior and engineering.

Particle Swarm Optimization combines rigid paradigms with more flexible social paradigms -social-psychological principles.  Swarm optimization provides insights into social behavior that was not easily recognized before.

PSO It has been used in many engineering applications, in art installations, to compose music, and to model organizations and markets. The first description of the particle swarm optimization algorithm was 1995 and was presented by James Kennedy and Russell C. Barnhart.

Kennedy and Barnhart observed that individuals naturally seem to solve problems most effectively by speaking with other individual.  Each individual remembers a situation where they were highly effective in solving a specific problem. This information is presented to others in the group (swarm.)  At the same time the individual is able to interact with and observe others in the swarm and note the optimum solution for the same problem.  This may possibly be a superior solution that may already exist in the swarm, or in areas of the swarm. As these individuals interact with the swarm and are a reflection of the swarm, their behaviors, attitudes, and beliefs, change to ever-greater levels of effectiveness in the solving of problems.  In this algorithm any individual may be a potential or “ candidate” solution. This is known as a particle, hence the name “particle swarm.”

It is amazing to observe the changes that take place in the “swarm” of particles. It is as if individuals are moving toward one another in a social space where rapport and commonality of thought co-exist. (sociocognitive space.)

As with many mathematically based techniques (PSO) has evolved greatly since its development. The form in which it is presently applied is barely recognizable when compared to the original version of the algorithm.

This type of algorithm can be applied in LHAGT since that approach to problem solving and decision making  explores the use of problem solving skills, human resource skills and many other variables, interacting and continually elevating the process towards ever more productivity and effectiveness at a lower and lower cost.  This is the essence of Particle Swarm Optimization.

Even if an individual is not a mathematician they can  make sense out of this. What be required is the willingness of the individual to seek out experts, mathematicians, statisticians, and computer scientists to solve extreme problems using PSO.  PSO is one of the most valuable tools for  solve extreme problems and there are few downsides. As extreme problems of ever-greater difficulty present themselves, new modifications are developed for multi-objective optimization.  This solution can be applied to find solutions for linear or non-linear obstacles. Mathematicians have also modified PSOs to create new versions known as “repulsive particle swarm optimization.

The master decision maker is defined as such because he/she has the ability and willingness to think out of the box and delegate to others who can do the same.

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Lewis Harrison is an author, content rich, motivational speaker, mentor and coach. You can reach him at LewisCoaches@gmail.com

Lewis is an explicator and demystifier on the links between spirituality, game theory, quantum physics, economics, personal growth and human potential.

He often uses Swarm Optimization at trade shows where there are thousands of attendees.

He offers stress management programs throughout the United  States. Part of this company is  his corporate chair massage company, eventschairmassage.com provides seated and chair massage for stress management seminars and trainings as well to special events for  meeting planners and meeting professionals in New York City, New Jersey Las Veges, Los Angeles, Cleveland, Greensboro NC, Florida and other major meeting and conventions venues.