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An improved hybrid salp swarm optimization and African vulture optimization algorithm for global optimization problems and its applications in stock market prediction

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Abstract

Optimization is necessary for solving and improving the solution of various complex problems. Every meta-heuristic algorithm can have a weak point, and multiple mechanisms and methods can be used to overcome these weaknesses. Some algorithms perform well in the discovery phase and some algorithms perform better in the exploitation phase. Hybridizing algorithms can be a good solution to achieve a powerful algorithm, and hybridizing algorithms and mechanisms greedily lead to an increase in computational complexity and execution time. This paper presents a new and intelligent approach by hybridizing meta-heuristic algorithms and using different mechanisms simultaneously without significantly increasing the time complexity. For this purpose, salp swarm optimization (SSO) and the African vulture optimization algorithm (AVOA) have been hybridized to improve the optimization process of the modified choice function and learning automata mechanisms. In addition, two other improving mechanisms, named opposition-based learning (OBL) and β-hill climbing (BHC) technique, have been presented and integrated with the AVOA–SSA algorithm. Fifty-two standard benchmarks were used to test and evaluate the AVOA–SSA algorithm. Finally, an improved version of the extreme learning machine (ELM) classifier has been used with real stock market data for stock market prediction. The obtained results indicate the excellent and acceptable performance of the AVOA–SSA algorithm in `solving optimization problems and have been able to achieve high-quality solutions. According to the results obtained from the AVOA–SSA algorithm, in comparison to global optimization problems, the AVOA–SSA algorithm has been able to obtain the best results in 21 functions out of 23 standard benchmarks. Also, against CEC2017 problems, it has been able to perform best in 26 out of 29 functions. In addition, the AVOA–SSA algorithm has been able to perform better than other compared algorithms in all five datasets evaluated in the stock market.

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Data availability

The data used 1001 on September 3, 2018, and five stock indices, DAX, FTSE, IBEX, NIKKEI, and S&P, were collected from the website (https://www.investing.com) for forecasting the stock market and used. FTSE 100 Index (FTSE): https://www.investing.com/indices/uk-100, S&P 500 Index (SPX): https://www.investing.com/indices/us-spx-500, IBEX Ltd Stock Price Today | NASDAQ IBEX Live Ticker: https://www.investing.com/equities/ibex-holdings-ltd, Nikkei 225 Index (N225): https://www.investing.com/indices/japan-ni225, DAX Stock Price | Global X DAX Germany ETF: https://www.investing.com/etfs/recon-capital-dax-germany, [Available access: September 3, 2018].

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Authors and Affiliations

  1. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Ali Alizadeh, Farhad Soleimanian Gharehchopogh & Mohammad Masdari

  2. Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran

    Ahmad Jafarian

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  1. Ali Alizadeh
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  2. Farhad Soleimanian Gharehchopogh
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  3. Mohammad Masdari
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  4. Ahmad Jafarian
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Correspondence to Farhad Soleimanian Gharehchopogh.

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Alizadeh, A., Gharehchopogh, F.S., Masdari, M. et al. An improved hybrid salp swarm optimization and African vulture optimization algorithm for global optimization problems and its applications in stock market prediction. Soft Comput 28, 5225–5261 (2024). https://doi.org/10.1007/s00500-023-09299-y

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