In our newest research paper, A Simulated annealing (SA)-based algorithm which basically mimics a physical annealing process and is originated from the physic-chemical science, now, wonderfully, help us optimize one of the well-known combinatorial problems like 0-1 Knapsack Problem (KP01). But, do not make a mistake! this SA is not an ordinary SA-based solver, on the other hand, the new proposed SA not only is a population-based version unlike the others but also is modified purposefully to solve KP01 efficiently at the most possible strength. Do not forget! the new population-based SA (PSA) has to be respectful for two things: (1) Efficient Repair & Improvement (RI) operator exists in the literature which has been proposed by the researchers, (2) Efficient crossover & mutation functions to search the neighborhood.
Solving the problems with valuable information for the computational science by the methods like SA is not a normal event that we heard of and let it go! Now, everyday, every moment, we encounter metaheuristics and heuristics which have to be noticed; what is the golden property of these metaheuristics which make them powerful in solving even the hardest problem with the largest sizes in comparison with exact solvers?
This golden property is the responsible for this efficiency without having specific mathematical explanation which is not stated clearly yet. I believe that this golden property can be specified by investigating the various metaheuristics to discover their main similarities and differences to understand their structure; this is the key for our story!
To read more official and technical words, visit our new paper at: https://link.springer.com/article/10.1007%2Fs00366-020-01240-3
