摘要：
Brain storm optimization (BSO) is a population-based intelligence algorithm for optimization problems, which has attracted researchers' growing attention due to its simplicity and efficiency. An improved BSO, called CIBSO, is presented in this article. First of all, a new grouping method, in which the population is partitioned into chunks according to the fitness and recombined to groups, is developed to balance each group with same quality-level. Afterwards, a new mutation strategy is designed in CIBSO and a learning mechanism is used to adaptively select appropriate strategy. Experiments on the CEC2014 test suite indicate that CIBSO is better or at least competitive performance against the compared BSO variants.
作者机构：
[Guo, Jinglei; Meng, Haoyu; Shi, Zeyuan; Guo, JL] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
会议名称：
Genetic and Evolutionary Computation Conference (GECCO)
会议时间：
JUL 15-19, 2023
会议地点：
Lisbon, PORTUGAL
会议主办单位：
[Meng, Haoyu;Guo, Jinglei;Shi, Zeyuan] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
关键词：
ant colony optimization;traveling salesman problem;outlier;route construction
摘要：
Constructing a finite set of candidates for each node has been proved that it is an effective means in ant colony optimization (ACO) for solving the travelling salesman problem (TSP). However, some neighbor nodes in the optimal routes are two nodes with large separation distance. To solve this problem, this paper proposes an ACO with pre -exploration of outliers (ACO-EO). The techniques in ACO-EO include: a) the outliers selection, b) pre -exploration adjacent nodes for outliers. To verify the effectiveness of the ACO-EO, a number of experiments are conducted using 30 benchmark instances (ranging from 101 nodes to 1784 nodes in topologies) taken from the well-known TSPLIB. From the comparison with state-of-the-art ACO-based methods, ACO-EO outperforms these competitors in terms of convergence and solution accurancy.
摘要：
The aim of multi-objective particle swarm optimizer (MOPSO) is to find an accurate and well-distributed approximation of the true Pareto Front (PF). The intrinsic character of PSO puts convergence first, which can cause great loss of population diversity. How to maintain the convergence and diversity simultaneously is an essential issue for MOPSO. In this paper, we propose a niche based multi-objective particle swarm optimizer (NMOPSO) to balance the convergence and diversity. First, a niche based on the Euclidean distance is constructed for each particle, then the leading particle is chosen out either from the niche or from the whole swarm. After that, two position update strategies are designed to update the position of each particle. The position update strategies provide two guiding models for leaders, one is utilizing the difference vector between the leader and the current particle, the other is directly taking some components of leaders. Three well-known test suites are employed to verify the performance of NMOPSO. Compared with three popular MOPSOs, simulation results show that NMOPSO performs better on most of test problems.
摘要：
Differential evolution (DE) has been a popular algorithm for its simple structure and few control parameters. However, there are some open issues in DE regrading its mutation strategies. An interesting one is how to balance the exploration and exploitation behaviour when performing mutation, and this has attracted a growing number of research interests over a decade. To address this issue, this paper presents a triangular Gaussian mutation strategy. This strategy utilizes the physical positions and the fitness differences of the vertices in the triangular structure. Based on this strategy, a triangular Gaussian mutation to DE and its improved version (ITGDE) are suggested. Empirical studies are carried out on the 20 benchmark functions and show that, in comparison with several state-of-the-art DE variants, ITGDE obtains significantly better or at least comparable results, suggesting the proposed mutation strategy is promising for DE.
摘要：
As a population-based intelligence algorithm, fireworks algorithm simulates the fireworks' explosion process to solve optimisation problem. A comprehensive study on enhanced fireworks algorithm (EFWA) reveals that the explosion operator generates too much sparks for the best firework limits the exploration ability. A hybrid version of EFWA (HFWA_DE) is proposed by adding the differential evolution (DE) operator. In HFWA_DE, the population is divided into two subpopulations, then each subpopulation evolves with FWA operator and DE operator separately and exchanges the elitist individual. Experiments on 20 well-known benchmark functions are conducted to illustrate the performance of HFWA_DE. The results turn out HFWA_DE outperforms some state-of-the-art FWAs on most testing functions.
摘要：
Differential evolution (DE) is one of the most powerful and effective evolutionary algorithms for solving global optimization problems. However, just like all other metaheuristics, DE also has some drawbacks, such as slow and/or premature convergence. This paper proposes a new subset-to-subset selection operator to improve the convergence performance of DE by randomly dividing target and trial populations into several subsets and employing the ranking-based selection operator among corresponding subsets. The proposed framework gives more survival opportunities to trial vectors with better objective function values. Experimental results show that the proposed method significantly improves the performance of the original DE algorithm and several state-of-the-art DE variants on a series of benchmark functions.
会议名称：
IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
会议时间：
JUL 08-13, 2018
会议地点：
Rio de Janeiro, BRAZIL
会议主办单位：
[Jiang, Shouyong;Kaiser, Marcus;Krasnogor, Natalio] Newcastle Univ, Sch Comp, Newcastle Upon Tyne NE4 5TG, Tyne & Wear, England.^[Wan, Shuzhen] China Three Gorges Univ, Sch Comp Sci & Informat Technol, Yichang, Peoples R China.^[Guo, Jinglei] Cent China Normal Univ, Dept Comp Sci, Wuhan, Hubei, Peoples R China.^[Yang, Shengxiang] De Montfort Univ, Sch Comp Sci & Informat, Leicester, Leics, England.
会议论文集名称：
IEEE Congress on Evolutionary Computation
摘要：
Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic mul-tiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.
作者机构：
[Jiang, Shouyong; Kaiser, Marcus; Krasnogor, Natalio] Newcastle Univ, Sch Comp, Newcastle Upon Tyne, Tyne & Wear, England.;[Guo, Jinglei] Cent China Normal Univ, Dept Comp Sci, Wuhan, Peoples R China.;[Yang, Shengxiang] De Montfort Univ, Sch Comp Sci & Informat, Leicester, Leics, England.
会议名称：
Genetic and Evolutionary Computation Conference (GECCO)
会议时间：
JUL 15-19, 2018
会议地点：
Kyoto, JAPAN
会议主办单位：
[Jiang, Shouyong;Kaiser, Marcus;Krasnogor, Natalio] Newcastle Univ, Sch Comp, Newcastle Upon Tyne, Tyne & Wear, England.^[Guo, Jinglei] Cent China Normal Univ, Dept Comp Sci, Wuhan, Peoples R China.^[Yang, Shengxiang] De Montfort Univ, Sch Comp Sci & Informat, Leicester, Leics, England.
关键词：
less detectable environment (LDE);environmental changes;dynamic multiobjective optimisation
摘要：
Multiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective optimisation, there is a lack of work on the detectability of environmental changes and how this affects the performance of evolutionary algorithms. This is not intentionally left blank but due to the unavailability of suitable test cases to study. To bridge the gap, this work presents several scenarios where environmental changes are less likely to be detected. Our experimental studies suggest that the less detectable environments pose a big challenge to evolutionary algorithms. CCS CONCEPTS • Theory of computation → Evolutionary algorithms; • Computing methodologies → Optimization algorithms; KEYWORDS less detectable environment (LDE), environmental changes, dynamic multiobjective optimisation ACM Reference Format:
期刊：
Proceedings - 2017 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration, ICIICII 2017,2017年2017-December:262-265
作者机构：
[Wu, Yong] School of Automation, Wuhan University of Technology, Wuhan, China;[Guo, Jinglei] School of Computer, Central China Normal University, Wuhan, China
会议名称：
IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)
会议时间：
JUL 24-29, 2016
会议地点：
Vancouver, CANADA
会议主办单位：
[Jiang, Shouyong;Yang, Shengxiang] De Montfort Univ, Sch Comp Sci, Leicester LE1 9BH, Leics, England.^[Guo, Jinglei] Cent China Normal Univ, Dept Comp Sci, Wuhan, Peoples R China.
会议论文集名称：
IEEE Congress on Evolutionary Computation
摘要：
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a number of single-objective problems and solves them collaboratively. Since its introduction, MOEA/D has gained increasing research interest and has become a benchmark for validating new designed algorithms. Despite that, some recent studies have revealed that MOEA/D faces some difficulties to solve problems with complicated characteristics. In this paper, we study the influence of the penalty-based boundary intersection (PBI) approach, one of the most popular decomposition approaches used in MOEA/D, on individuals' convergence and diversity, showing that the fixed same penalty value for all the subproblems is not very sensible. Based on this observation, we propose to use adaptive penalty values to enhance the balance between population convergence and diversity. Experimental studies show that the proposed adaptive PBI can generally improve the performance of the original PBI when solving the problems considered in this paper.
期刊：
International Journal of Wireless and Mobile Computing,2016年11(3):190-197 ISSN：1741-1084
通讯作者：
Guo, Jinglei(guojinglei@mail.ccnu.edu.cn)
作者机构：
[Li, Zhijian; Guo, Jinglei] School of Computer Science, Central China Normal University, Wuhan, 430079, China;[Yang, Shengxiang] Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort Univesity, Leicester, LE1 9BH, United Kingdom
通讯机构：
School of Computer Science, Central China Normal University, Wuhan, China
摘要：
Differential evolution (DE) is one of the most powerful and popular evolutionary algorithms for real parameter global optimisation problems. However, the performance of DE highly depends on the selection of control parameters, e.g. the population size, scaling factor and crossover rate. How to set these parameters is a challenging task because they are problem dependent. In order to tackle this problem, a JADE variant, denoted CJADE, is proposed in this paper. In the proposed algorithm, the successful parameters are clustered with the k-means clustering algorithm to reduce the impact of poor parameters. Simulation results show that CJADE is better than, or at least comparable to, several state-of-the-art DE algorithms.
