An improved multi-objective population-based extremal optimization algorithm with polynomial mutation

Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.

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Multi-Objective Optimization Based on Brain Storm Optimization Algorithm

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2013070101 ◽

2013 ◽

Vol 4 (3) ◽

pp. 1-21 ◽

Cited By ~ 37

Author(s):

Yuhui Shi ◽

Jingqian Xue ◽

Yali Wu

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Solution Space ◽

Population Based ◽

Multi Objective Optimization ◽

Multi Objective ◽

Brain Storm Optimization ◽

Objective Space ◽

Different Characteristics ◽

Single Objective

In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objective space instead of in the solution space in the original brain storm optimization algorithm for solving single objective optimization problems. Two versions of multi-objective brain storm optimization algorithm with different characteristics of diverging operation were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that the proposed multi-objective brain storm optimization algorithm is a very promising algorithm, at least for solving these tested multi-objective optimization problems.

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An Improved Artificial Electric Field Algorithm for Multi-Objective Optimization

Processes ◽

10.3390/pr8050584 ◽

2020 ◽

Vol 8 (5) ◽

pp. 584

Author(s):

Hemant Petwal ◽

Rinkle Rani

Keyword(s):

Electric Field ◽

Density Estimation ◽

Optimization Problems ◽

Population Based ◽

Population Diversity ◽

Multi Objective Optimization ◽

Local Optima ◽

Multi Objective ◽

Polynomial Mutation ◽

Optimum Solution

Real-world problems such as scientific, engineering, mechanical, etc., are multi-objective optimization problems. In order to achieve an optimum solution to such problems, multi-objective optimization algorithms are used. A solution to a multi-objective problem is to explore a set of candidate solutions, each of which satisfies the required objective without any other solution dominating it. In this paper, a population-based metaheuristic algorithm called an artificial electric field algorithm (AEFA) is proposed to deal with multi-objective optimization problems. The proposed algorithm utilizes the concepts of strength Pareto for fitness assignment and the fine-grained elitism selection mechanism to maintain population diversity. Furthermore, the proposed algorithm utilizes the shift-based density estimation approach integrated with strength Pareto for density estimation, and it implements bounded exponential crossover (BEX) and polynomial mutation operator (PMO) to avoid solutions trapping in local optima and enhance convergence. The proposed algorithm is validated using several standard benchmark functions. The proposed algorithm’s performance is compared with existing multi-objective algorithms. The experimental results obtained in this study reveal that the proposed algorithm is highly competitive and maintains the desired balance between exploration and exploitation to speed up convergence towards the Pareto optimal front.

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