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

Abstract

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.

A survey on binary metaheuristic algorithms and their engineering applications

“…Hassan et al proposed stochastic travelling advisor problem (STAP) in network optimization (Hassan et al. 2020b ), and it is defined for an advisory group who chooses a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. STAP is a typical binary optimization which has a stochastic nature in travelling and advising time, and binary GSK solves it.…”

“…( 2016b ) Binary PSO Lot Sizing Fast Medium High Hassan et al. ( 2020b ) Binary GSK STAP Medium Medium Low Hassan et al. ( 2021b ) DBGSK TDP Medium Medium Low …”

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

“…Many of these algorithms is gaining widespread acceptance in different domains of machine learning and artificial inteligence, such as the use of such algorithms in neuro-evolution in Reinforcement Learning (RL) settings [87] or usage of evolutionary control parameters in Automated Machine Learning [87]. In particular, we have observed widespread use of AGSK in transportation problems [88], [89], path planning [90], knapsack problems [91], [92], electrochemical systems such as photo-voltaic cells [93], [94], engineering problems like fault diagnostics in power systems [95], as well as adoption in machine learning [96] and RL techniques [97]. As a result, it is reasonable to claim that constrained evolutionary algorithms offer enormous potential in the application of many engineering design challenges [1], [98]- [100].…”

UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

“…Gaining-Sharing Knowledge algorithm (GSK) is a nature inspired algorithm based on human behavior, and how a single individual can share knowledge and learn from the others. Introduced in [19], GSK has been applied in several optimization problems as feature selection [24][25][26], scheduling problems [27][28][29], maximal covering model [30], parameter extraction of photovoltaic models [31] and combinatorial optimization problems as knapsack [32], travelling salesman problem [33], travelling advisor problem [34], and transportation problem [35].…”