Abstract
Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This work proposes a hybrid SA with EMC to divert the search effectively to another promising region. Moreover, a Tabu list memory applied to avoid cycling. Experimental results showed that the solution quality has enhanced using SA-EMCQ by escaping the search space from local optimum to another promising region space. In addition, the results showed that our proposed technique has outperformed the standard SA and gave comparable results to other approaches in the literature when tested on ITC2007-Track3 university course timetabling datasets.
Highlights
Simulated Annealing, known as (SA) is a stochastic optimization technique inspired by Metropolis algorithm for statistical mechanics (Metropolis et al, 1953; Van Laarhoven & Aarts, 1987)
Experimental and Results This section presents our experiment results to test the performances of our EMCQ in comparison to others as describes in the literature review
This paper focused on reviewing several heuristics and meta-heuristics algorithms that implemented in the literature to solve the combinatorial optimization problems, especially university course timetabling problem
Summary
Simulated Annealing, known as (SA) is a stochastic optimization technique inspired by Metropolis algorithm for statistical mechanics (Metropolis et al, 1953; Van Laarhoven & Aarts, 1987). As in many metaheuristics techniques, SA has the drawback of revisiting the same solution (recycling) and trapped in local optimum. This will lead to a longer time to find reasonable good solution. 3) Exponential Monte Carlo with counter (EMCQ) has extend the EMC acceptance criterion to accept the worse solution depending on the solution quality, and a consecutive non-improving iterations number as a counter to adaptively accept the worst solution. The main contribution of this hybrid approach is to save the moves during the search, in order to avoid cyclic move, by keeping the accepted move in a Tabu list for a certain number of iterations. This work presents a hybrid solution using SA with EMC-counter and Tabu list memory to addresses the limitations of SA trapped in a local optimum by escaping from local optimum solution
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