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A multi-objective Artificial Bee Colony for optimizing multi-objective problems
2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), 2010This work proposes a multi-objective artificial bee colony (MOABC) for optimizing problems with multiple objectives. We have adapted the original Artificial Bee Colony (ABC) algorithm to multi objective problems with a grid-based approach for maintaining and adaptively assessing the Pareto front.
Ramin Hedayatzadeh +3 more
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Simulated Annealing for Multi Objective Optimization Problems
1994In the last decade some large scale combinatorial optimization problems have been tackled by way of a stochastic technique called ‘simulated annealing’ first proposed by Kirkpatrick et al. (1983). This technique has proved to be a valid tool to find acceptable solutions for problems whose size makes impossible any exact solution method.
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An improved multi-objective particle swarm optimizer for multi-objective problems
Expert Systems with Applications, 2010This paper proposes an improved multi-objective particle swarm optimizer with proportional distribution and jump improved operation, named PDJI-MOPSO, for dealing with multi-objective problems. PDJI-MOPSO maintains diversity of new found non-dominated solutions via proportional distribution, and combines advantages of wide-ranged exploration and ...
Shang-Jeng Tsai +5 more
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2012
Multi-objective particle swarm optimization (MOPSO) is an optimization technique inspired by bird flocking, which has been steadily gaining attention from the research community because of its high convergence speed. However, faced with multi-objective problems, adaptations are needed. Deeper researches must be conducted on its key steps, such as guide
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Multi-objective particle swarm optimization (MOPSO) is an optimization technique inspired by bird flocking, which has been steadily gaining attention from the research community because of its high convergence speed. However, faced with multi-objective problems, adaptations are needed. Deeper researches must be conducted on its key steps, such as guide
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Evolutionary Computation, 2004
In this paper, an orthogonal multi-objective evolutionary algorithm (OMOEA) is proposed for multi-objective optimization problems (MOPs) with constraints. Firstly, these constraints are taken into account when determining Pareto dominance. As a result, a strict partial-ordered relation is obtained, and feasibility is not considered later in the ...
Sanyou Y, Zeng +2 more
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In this paper, an orthogonal multi-objective evolutionary algorithm (OMOEA) is proposed for multi-objective optimization problems (MOPs) with constraints. Firstly, these constraints are taken into account when determining Pareto dominance. As a result, a strict partial-ordered relation is obtained, and feasibility is not considered later in the ...
Sanyou Y, Zeng +2 more
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Applied Intelligence, 2016
This paper proposes a multi-objective version of the recently proposed Ant Lion Optimizer (ALO) called Multi-Objective Ant Lion Optimizer (MOALO). A repository is first employed to store non-dominated Pareto optimal solutions obtained so far. Solutions are then chosen from this repository using a roulette wheel mechanism based on the coverage of ...
Mirjalili, Seyedali +2 more
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This paper proposes a multi-objective version of the recently proposed Ant Lion Optimizer (ALO) called Multi-Objective Ant Lion Optimizer (MOALO). A repository is first employed to store non-dominated Pareto optimal solutions obtained so far. Solutions are then chosen from this repository using a roulette wheel mechanism based on the coverage of ...
Mirjalili, Seyedali +2 more
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Computers & Industrial Engineering, 2019
Abstract In this paper, we extend the chicken swarm optimization (CSO) to solve multi-objective optimization problems. Our extention aims to balance between diversity and convergence when searching for the optimal Pareto solutions. We use aggregation function to define the social hierarchy and simulate the behavior of chickens during the search for ...
Djaafar Zouache +3 more
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Abstract In this paper, we extend the chicken swarm optimization (CSO) to solve multi-objective optimization problems. Our extention aims to balance between diversity and convergence when searching for the optimal Pareto solutions. We use aggregation function to define the social hierarchy and simulate the behavior of chickens during the search for ...
Djaafar Zouache +3 more
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Taguchi's method for multi-objective optimization problems
International Journal of RF and Microwave Computer-Aided Engineering, 2012Taguchi's method is a quality design technique whose applications in numerical single-objective optimization have been recently exploited. In this article, a novel multi-objective (MO) algorithm based on Taguchi's technique is illustrated and its performances assessed.
E. Agastra +3 more
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Multi-objective Jaya Algorithm for Solving Constrained Multi-objective Optimization Problems
2019Solving multi-objective optimization problems (MOPs) is a challenging task since they conflict with each other. In addition, incorporation of constraints to the MOPs, called CMOPs for a short, increases their complexity. Traditional multi-objective evolutionary algorithms (MOEAs) treat multiple objectives as a whole while solving them.
Y. Ramu Naidu +2 more
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Multi-objective Optimization of Problems with Epistemic Uncertainty
2005Multi-objective evolutionary algorithms (MOEAs) have proven to be a powerful tool for global optimization purposes of deterministic problem functions. Yet, in many real-world problems, uncertainty about the correctness of the system model and environmental factors does not allow to determine clear objective values.
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