Improving the JADE algorithm by clustering successful parameters

Differential evolution (DE) is one of the most powerful and popular evolutionary algorithms for real parameter global optimisation problems. However, the performance of DE greatly 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 with, several state-of-the-art DE algorithms

Accelerating differential evolution based on a subset-to-subset survivor selection operator

The file attached to this record is the author's final peer reviewed version.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

Triangular Gaussian mutation to differential evolution

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

An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem(MOP) into a number of single-objective subproblems. Penalty boundary intersection (PBI) in MOEA/D is one of the most popular decomposition approaches and has attracted significant attention. In this paper, we investigate two recent improvements on PBI, i.e. adaptive penalty scheme (APS) and subproblem-based penalty scheme (SPS), and demonstrate their strengths and weaknesses. Based on the observations, we further propose a hybrid penalty scheme (HPS), which adjusts the PBI penalty factor for each subproblem in two phases, to ensure the diversity of boundary solutions and good distribution of intermediate solutions. HPS specifies a distinct penalty value for each subproblem according to its weight vector. All the penalty values of subproblems increase with the same gradient during the first phase, and they are kept unchanged during the second phase

Less detectable environmental changes in dynamic multiobjective optimisation

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

An empirical study of dynamic triobjective optimisation problems

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 multiobjective 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

AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, the performance of these algorithms depends largely on problem characteristics. There is a need to improve these algorithms for wide applicability. References, often specified by the decision maker’s preference in different forms, are very effective to boost the performance of algorithms. This paper proposes a novel framework for effective use of references to strengthen algorithms. This framework considers references as search targets which can be adjusted based on the information collected during the search. The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems. The proposed algorithm is compared with state-of-the-arts on a wide range of problems with diverse characteristics. The comparison and extensive sensitivity analysis demonstrate that the proposed algorithm is competitive and robust across different types of problems studied in this paper

Glucagon-like peptide 1 decreases lipotoxicity in non-alcoholic steatophepatitis

OBJECTIVE: Insulin resistance and lipotoxicity are pathognomonic in non-alcoholic steatohepatitis (NASH). Glucagon-like peptide-1 (GLP-1) analogues are licensed for type 2 diabetes, but no prospective experimental data exists in NASH. This study determined the effect of a long-acting GLP-1 analogue, liraglutide, on organ-specific insulin sensitivity, hepatic lipid handling and adipose dysfunction in biopsy-proven NASH.DESIGN: 14 patients were randomised to 1.8mg liraglutide or placebo for 12-weeks of the mechanistic component of a double-blind, randomized, placebo-controlled trial (ClinicalTrials.gov-NCT01237119). Patients underwent paired hyperinsulinaemic euglycaemic clamps, stable isotope tracers, adipose microdialysis and serum adipocytokine/metabolic profiling. In vitro isotope experiments on lipid flux were performed on primary human hepatocytes.RESULTS: Liraglutide reduced BMI (-1.9 vs. +0.04 kg/m2;p&lt;0.001), HbA1c (-0.3 vs. +0.3%;p&lt;0.01), cholesterol-LDL (-0.7 vs. +0.05 mmol/L;p&lt;0.01), ALT (-54 vs -4.0 IU/L;p&lt;0.01) and serum leptin, adiponectin, and CCL-2 (all p&lt;0.05). Liraglutide increased hepatic insulin sensitivity (-9.36 vs. -2.54% suppression of hepatic endogenous glucose production with low-dose insulin;p&lt;0.05). Liraglutide increased adipose tissue insulin sensitivity enhancing the ability of insulin to suppress lipolysis both globally (-24.9 vs. +54.8 pmol/L insulin required to ½ maximally suppress serum NEFA; p&lt;0.05), and specifically within subcutaneous adipose tissue (p&lt;0.05). In addition, liraglutide decreased hepatic DNL in-vivo (-1.26 vs. +1.30%; p&lt;0.05); a finding endorsed by the effect of GLP-1 receptor agonist on primary human hepatocytes (24.6% decrease in lipogenesis vs. untreated controls; p&lt;0.01).CONCLUSIONS: Liraglutide reduces metabolic dysfunction, insulin resistance and lipotoxicity in the key metabolic organs in the pathogenesis of NASH. Liraglutide may offer the potential for a disease-modifying intervention in NASH.</p

Glucagon-like peptide 1 decreases lipotoxicity in non-alcoholic steatophepatitis

Background & AimsInsulin resistance and lipotoxicity are pathognomonic in non-alcoholic steatohepatitis (NASH). Glucagon-like peptide-1 (GLP-1) analogues are licensed for type 2 diabetes, but no prospective experimental data exists in NASH. This study determined the effect of a long-acting GLP-1 analogue, liraglutide, on organ-specific insulin sensitivity, hepatic lipid handling and adipose dysfunction in biopsy-proven NASH.MethodsFourteen patients were randomised to 1.8mg liraglutide or placebo for 12-weeks of the mechanistic component of a double-blind, randomised, placebo-controlled trial (ClinicalTrials.gov-NCT01237119). Patients underwent paired hyperinsulinaemic euglycaemic clamps, stable isotope tracers, adipose microdialysis and serum adipocytokine/metabolic profiling. In vitro isotope experiments on lipid flux were performed on primary human hepatocytes.ResultsLiraglutide reduced BMI (−1.9 vs. +0.04kg/m2; p<0.001), HbA1c (−0.3 vs. +0.3%; p<0.01), cholesterol-LDL (−0.7 vs. +0.05mmol/L; p<0.01), ALT (−54 vs. −4.0IU/L; p<0.01) and serum leptin, adiponectin, and CCL-2 (all p<0.05). Liraglutide increased hepatic insulin sensitivity (−9.36 vs. −2.54% suppression of hepatic endogenous glucose production with low-dose insulin; p<0.05). Liraglutide increased adipose tissue insulin sensitivity enhancing the ability of insulin to suppress lipolysis both globally (−24.9 vs. +54.8pmol/L insulin required to ½ maximally suppress serum non-esterified fatty acids; p<0.05), and specifically within subcutaneous adipose tissue (p<0.05). In addition, liraglutide decreased hepatic de novo lipogenesis in vivo (−1.26 vs. +1.30%; p<0.05); a finding endorsed by the effect of GLP-1 receptor agonist on primary human hepatocytes (24.6% decrease in lipogenesis vs. untreated controls; p<0.01).ConclusionsLiraglutide reduces metabolic dysfunction, insulin resistance and lipotoxicity in the key metabolic organs in the pathogenesis of NASH. Liraglutide may offer the potential for a disease-modifying intervention in NASH