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This paper investigates the use of elitism in chemical reaction optimization (CRO) to address its convergence performance in difficult problems. We focus on problems with complex and highly discontinuous solution space. In such problems CRO's convergence performance tend to be sluggish as the algorithm repeatedly digress from the bestfound solution characteristics in search for solutions in different areas in the problem's solution space. A complex road network design problem is used to demonstrate this issue and experiment with the impact of elitism on algorithm convergence. Elitism has been used successfully in evolutionary algorithms. Results show that its use in CRO improves algorithm convergence performance drastically. However, due to CRO's tendency to have diminishing population of molecules in such problems, the use of a larger list of elite solutions appears to be ineffective in improving the algorithm performance beyond the initial gains from introducing elitism. We investigate the reasons behind this observation and point to possible solutions. [ABSTRACT FROM AUTHOR]

Markov chain traffic assignment (MCTA) is a relatively recent approach for modeling traffic conditions in a road network. It results in traffic density measurements that express congestion levels in the network at the road level. While the stochastic approach is powerful analytically, the resulting traffic density does not readily lend itself to emissions estimation. Typically, average-speed emission models are utilized to estimate emissions on an aggregate network level. However, while this is easily done in user equilibrium (UE) models using the Bureau of Public Roads (BPR) relationship to derive vehicles' average speed from traffic flow, no such relationship exists for traffic density. We highlight the mathematical challenge here and propose an approach to estimate vehicles' average speed based on traffic density obtained from MCTA. The average speeds are then used to estimate vehicles' Greenhouse Gas emissions using average-speed emission estimation models. This approach can be applied on a road level or aggregated to a network level. We utilize the BPR relationship to build a linear piece-wise approximation function and illustrate its use through two widely used average-speed emission estimation models: TRANSYT-7F and COPERT v5. [ABSTRACT FROM AUTHOR]