Multi-Objective Material Generation Algorithm (MOMGA) for Optimization Purposes

Nouhi, B.; Khodadadi, Nima; Azizi, Mahdi; Talatahari, Siamak; Gandomi, Amir H.

doi:10.1109/access.2022.3211529

Cited by 14 publications

(1 citation statement)

References 55 publications

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“…The multi-objective version of MGA for solving multi-objective optimization problems was presented by Nouhi et al 47 . MOMGA has three new mechanisms for solving multi-objective optimization.…”

Section: Methodsmentioning

confidence: 99%

Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

Samare Hashemi,

Robati,

Kazerooni

2024

Sci Rep

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The optimal operation of the multi-purpose reservoir system is a difficult, and, sometimes, non-linear problem in multi-objective optimization. By simulating biological behavior, meta-heuristic algorithms scan the decision space and can offer a set of points as a group of solutions to a problem. Because it is essential to simultaneously optimize several competing objectives and consider relevant constraints as the main problem in many optimization problems, researchers have improved their ability to solve multi-objective problems by developing complementary multi-objective algorithms. Because the AHA algorithm is new, its multi-objective version, MOAHA (multi-objective artificial hummingbird algorithm), was used in this study and compared with two novel multi-objective algorithms, MOMSA and MOMGA. Schaffer and MMF1 were used as two standard multi-objective benchmark functions to gauge the effectiveness of the proposed method. Then, for 180 months, the best way to operate the reservoir system of the Karun River basin, which includes Karun 4, Karun 3, Karun 1, Masjed-e-Soleyman, and Gotvand Olia dams to generate hydropower energy, supply downstream demands (drinking, agriculture, industry, environmental), and control flooding was examined from September 2000 to August 2015. Four performance appraisal criteria (GD, S, Δ, and MS) and four evaluation indices (reliability, resiliency, vulnerability, and sustainability) were used in Karun's multi-objective multi-reservoir problem to evaluate the performance of the multi-objective algorithm. All three algorithms demonstrated strong capability in criterion problems by using multi-objective algorithms’ criteria and performance indicators. The large-scale (1800 dimensions) of the multi-objective operation of the Karun Basin reservoir system was another problem. With a minimum of 1441.71 objectives and an average annual hydropower energy manufacturing of 17,166.47 GW, the MOAHA algorithm demonstrated considerable ability compared to the other two. The final results demonstrated the MOAHA algorithm’s excellent performance, particularly in difficult and significant problems such as multi-reservoir systems' optimal operation under various objectives.

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Section: Methodsmentioning

confidence: 99%

Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

Samare Hashemi,

Robati,

Kazerooni

2024

Sci Rep

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Optimum Design of Dam Structures Using Multi-objective Chaos Game Optimization Algorithm

Jafari

Saraee

Nouhi

et al. 2023

Studies in Systems, Decision and Control

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Multi-objective chaos game optimization

Khodadadi

Abualigah

Al-Tashi

et al. 2023

Neural Comput & Applic

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The Chaos Game Optimization (CGO) has only recently gained popularity, but its effective searching capabilities have a lot of potential for addressing single-objective optimization issues. Despite its advantages, this method can only tackle problems formulated with one objective. The multi-objective CGO proposed in this study is utilized to handle the problems with several objectives (MOCGO). In MOCGO, Pareto-optimal solutions are stored in a fixed-sized external archive. In addition, the leader selection functionality needed to carry out multi-objective optimization has been included in CGO. The technique is also applied to eight real-world engineering design challenges with multiple objectives. The MOCGO algorithm uses several mathematical models in chaos theory and fractals inherited from CGO. This algorithm's performance is evaluated using seventeen case studies, such as CEC-09, ZDT, and DTLZ. Six well-known multi-objective algorithms are compared with MOCGO using four different performance metrics. The results demonstrate that the suggested method is better than existing ones. These Pareto-optimal solutions show excellent convergence and coverage.

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Multi-Objective Material Generation Algorithm (MOMGA) for Optimization Purposes

Cited by 14 publications

References 55 publications

Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

Optimum Design of Dam Structures Using Multi-objective Chaos Game Optimization Algorithm

Multi-objective chaos game optimization

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