Evolutionary multiobjective optimization (EMO) methodologies have gained popularity in finding a representative set of Pareto optimal solutions the past decade and beyond. Several techniques been proposed specialized literature to ensure good convergence diversity obtained solutions. However, real world applications, decision maker is not interested overall front since final unique solution. Recently, there has an increased emphasis addressing decision-making task searching for most...

Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and efficiency in solving problems with two or three objectives. However, recent studies show that MOEAs face many difficulties when tackling involving a larger number of objectives as behavior becomes similar to random walk the search space since most individuals are nondominated respect each other. Motivated by interesting results decomposition-based approaches preference-based ones, we propose this paper new...

Software systems nowadays are complex and difficult to maintain due continuous changes bad design choices. To handle the complexity of systems, software products are, in general, decomposed terms packages/modules containing classes that dependent. However, it is challenging automatically remodularize improve their maintainability. The majority existing remodularization work mainly satisfy one objective which improving structure packages by optimizing coupling cohesion. In addition, most...

We propose in this paper to consider code-smells detection as a distributed optimization problem. The idea is that different methods are combined parallel during the process find consensus regarding of code-smells. To end, we used Parallel Evolutionary algorithms (P-EA) where many evolutionary with adaptations (fitness functions, solution representations, and change operators) executed, cooperative manner, solve common goal which An empirical evaluation compare implementation our P-EA...

There is a growing need for scalable search-based software engineering approaches that address problems where large number of objectives are to be optimized. Software refactoring one these sequence sought optimizes several metrics. Most the existing work uses set quality metrics evaluate design after applying operations, but current limited using maximum five We propose first time approach based on newly proposed evolutionary optimization method NSGA-III there 15 different In our approach,...

We propose a novel recommendation tool for software refactoring that dynamically adapts and suggests refactorings to developers interactively based on their feedback introduced code changes. Our approach starts by finding upfront set of non-dominated solutions using NSGA-II improve quality, reduce the number increase semantic coherence. The generated are analyzed our innovization component extract some interesting common features between them. Based this analysis, suggested ranked developer...

Code smells represent design situations that can affect the maintenance and evolution of software. They make system difficult to evolve. are detected, in general, using quality metrics some symptoms. However, selection suitable is challenging due absence consensus identifying code based on a set symptoms also high calibration effort determining manually threshold value for each metric. In this article, we propose treating generation code-smell detection rules as bilevel optimization problem....

Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. fact has made it, nowadays, one of the most powerful algorithms in solving mono-objective problems. In this paper, we propose multiobjective variant optimization, nondominated sorting an attempt to exploit tackling problems...

Dynamic Multi-objective Optimization (DMO) is a challenging research topic since the objective functions, constraints, and problem parameters may change over time. Several evolutionary algorithms have been proposed to deal with DMO problems. Nevertheless, they were restricted unconstrained or domain constrained In this work, we focus on dynamicty of constraints along time-varying functions. As very recent area, observed lack benchmarks that simultaneously take into account these...

Decomposition-based evolutionary algorithms using predefined reference points have shown good performance in many-objective optimization. Unfortunately, almost all experimental studies focused on problems having regular Pareto fronts (PFs). Recently, it has been that the of such is deteriorated when facing irregular PFs, as degenerate, discontinuous, inverted, strongly convex, and/or concave fronts. The main issue may not intersect with PF. Therefore, many researchers proposed to update aim...