MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems

Jangir, Pradeep; Buch, Hitarth; Mirjalili, Seyedali; Premkumar, M.

doi:10.1007/s12065-021-00649-z

Cited by 63 publications

(15 citation statements)

References 47 publications

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“…Recently, a lot of studies have been devoted to multiobjective swarm intelligence optimization algorithms. In general, a multi-objective swarm intelligence optimization algorithm involves two essential strategies, i.e., offspring generation strategy and elite selection MPA, Gaussian Mutation Non-dominated sorting, Crowded distance [64] MPA Non-dominated sorting, Fuzzy set method [65] MPA, Non-uniform mutation operator Non-dominated sorting, Niche approach [66] MPA Non-dominated sorting, Niche approach [67] MPA Non-dominated sorting, Crowded distance, [68] MPA, Gaussian perturbation Non-dominated sorting, Reference points Ours MPA, Gaussian perturbation, Non-dominated sorting, Reference points Competition mechanism based learning strategy strategy. Different algorithms may have different strategies.…”

Section: Multi-objective Swarm Intelligence Optimization Algorithmsmentioning

confidence: 99%

“…The multi-objective MPA proposed in [66] selects elite individuals by nondominated sorting and a niche approach. In [63,67], two versions of MOMPA based on crowding distance were put forward, however, its performance degrades when the number of objectives is more than two. In our previous work, we also proposed the MOMPA [68], which adopts the reference points based mechanism to select elite offsprings and achieves satisfactory performance in solution convergence and diversity for most multi-objective optimization problems, except for some complex multimodal problems.…”

Section: Multi-objective Swarm Intelligence Optimization Algorithmsmentioning

confidence: 99%

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Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

Chen

et al. 2022

Appl Intell

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Section: Multi-objective Swarm Intelligence Optimization Algorithmsmentioning

confidence: 99%

Section: Multi-objective Swarm Intelligence Optimization Algorithmsmentioning

confidence: 99%

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

Chen

et al. 2022

Appl Intell

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“…A storage and leader selection strategy were then combined into a single objective Ion Motion Optimization (IMO) approach to solving multiobjective problems. A group of unconstrained, constrained, and engineering benchmark functions was employed to assess the performance of the Multi-Objective IMO (MOIMO) [52][53][54][55][56][57][58][59][60][61][62]. The outcomes of MOIMO are compared with those of the Multi-Objective Dragonfly Algorithm (MODA), Multi-Objective Multi-Verse Optimizer (MOMVO), and Multi-Objective Ant Lion Optimizer (MOALO).…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Framework for Optimal Power Flow Problem of Hybrid Power Systems Considering Security Constraints

Pandya¹,

Sowmya

Premkumar

et al. 2022

IEEE Access

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The hybrid model of the power network infrastructure is an essential part of the sophisticated technology of the electrical network. For the traditional Optimal Power Flow (OPF) problem, thermal generators are typically employed to generate electricity. In this case, the amount of fuel required to produce power is constrained, and emissions from the network are frequently neglected. Renewable Energy Sources (RESs) have received increasing attention due to various potential characteristics such as cleanliness, diversity, and renewability. As an outcome, RESs are being integrated into the electrical grid at an increasing rate. The study in this paper proposes a techno-monetary investigation into the single-and multi-objective OPF, coordinating with RESs like wind, PhotoVoltaic (PV), and small hydropower units with hybrid PV. Moreover, the probability density functions of Weibull, Lognormal, and Gumble have been used independently to predict the required power in this paper. This paper considers an equilibrium optimizer for handling the OPF problem. The superior performance of the equilibrium optimizer is further verified with the results of both single-and multi-objective through comparative analysis with state-of-the-art counterparts, and the indications are that the proposed method can find better optimal solutions, use a lower number of generated solutions, and faster convergence with well distributed optimal Pareto front for multi-objective problems. The results are verified by employing a modified IEEE-30 bus hybrid power network, and performance comparisons are made among several well-established algorithms. Simulation findings show that the suggested strategy can achieve a reasonable compromise solution for different objectives.

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“…the optimization of multiple objective functions [6], [7]. These solutions are not simply the sum of weights, and each objective function is a mutually restrictive relationship.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning

Wang

Deng

2022

IEEE Access

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The traditional seagull optimization algorithm cannot handle multi-objective optimization problems, so a multi-objective quantum-inspired seagull optimization algorithm based on decomposition (MOQSOA/D) is proposed. Multi-objective computing and quantum computing are introduced into MOQSOA/D. MOQSOA/D transforms the multi-objective problem into multiple scalar optimization subproblems, and establishes a dynamic archive and a leadership archive at the same time. The Pareto solution of each sub-problem is stored in a dynamic archive, and the non-dominated Pareto solution is stored in the leader archive. While processing each sub-problem, each seagull is represented by a string of qubits, which is used to calculate the current seagull direction of flight, and a variable angular-distance rotation (VAR) gate is used to change the probability amplitude of the qubits, thereby updating the direction of flight. Penalty-based boundary intersection approach is introduced to determine whether the generated Pareto solution is retained. The proposed algorithm and six different algorithms were tested on 69 indicators, and the results show that the algorithm achieved better results in 40 indicators. In addition, a Unmanned Aerial Vehicle (UAV) path planning model in three-dimensional environment is designed to test the utility of MOQSOA/D, and the algorithm is compared with the other algorithm to demonstrate its effectiveness.INDEX TERMS Seagull optimization algorithm, multi-objective problem, qubits encoding, path planning.

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MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems

Cited by 63 publications

References 47 publications

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

Multi-Objective Optimization Framework for Optimal Power Flow Problem of Hybrid Power Systems Considering Security Constraints

A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning

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