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Investors always want to know about the profit and the risk that they will beget before buying some assets. Our main focus is getting the profit and theprobability of getting that profit using the differential evolution algorithmfor vanilla option pricing on data before and during COVID-19 pandemic.Therefore, we model the pricing of an option using a bi-objective optimizationproblem using data before and during COVID-19 pandemic for one year expirationdate. We change this problem into an optimization problem using adaptiveweighted sum method. We use metaheuristics algorithm like DifferentialEvolution (DE) algorithm to solve this bi-objective optimization problems. Inthis paper, we also use modification of Differential Evolution for gettingPareto optimal solutions on vanilla option pricing for all contract. Thealgorithm is called Combinational Mutation Strategy of Differential Evolution(CmDE) algorithm. The results of our algorithm are satisfactory close to thereal option price in the market data. Besides that, we also compare our resultwith the Black-Scholes results for validation. The results show that ourresults can approximate the real market options more accurate thanBlack-Scholes results. Hence, our bi-objective optimization using CombinationalMutation Strategy of Differential Evolution algorithm can be used toapproximate the market real vanilla option pricing before and during COVID-19pandemic.

The Combinational Mutation Strategy of Differential Evolution Algorithm  for Pricing Vanilla Options and Its Implementation on Data during Covid-19  Pandemic