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Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of. Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization. MULTI-OBJECTIVE OPTIMIZATION USING EVOLUTIONARY ALGORITHMS. Front Cover. Kalyanmoy Deb. Wiley India Pvt. Limited, - pages.
In recent times, many concerns have been raised when dealing with emerging technologies in industrial and engineering optimization which present themselves in a multiobjective MO setting [ 1 — 5 ]. Some of the issues are lack of computational power, insufficient computational techniques, and poor quality of obtained solution. Strategies in MO optimization can be rudimentarily classified into three groups. The second class of techniques are known as the weighted or scalarization techniques. During the application of these methods, the objective functions are aggregated into a single weighted function which is then solved for various scalar weight values. Some well-known scalarization techniques include the weighted sum method [ 8 ] and normal-boundary intersection method NBI [ 9 ]. Using these techniques, the scalars or weights are used to consign relative tradeoffs to the objectives by the aggregation procedure.
Carlos M. Fonseca and Peter J. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3 1 :1—16, Goldberg and J. Genetic algorithms with sharing for multimodal function optimization.
Lawrence Erlbaum. Google Scholar 3. David E. Addison-Wesley, Reading, Massachusetts, Google Scholar 4. Hajela and C. Genetic search strategies in multicriterion optimal design.
Structural Optimization, —, CrossRef Google Scholar 5. Jeffrey Horn and Nicholas Nafpliotis.
Multiobjective optimization using the niched pareto genetic algorithm. Google Scholar 6.
A niched pareto genetic algorithm for multiobjective optimization. Google Scholar 7. Silvano Martello and Paolo Toth. Knapsack Problems: Algorithms and Computer Implementations. Wiley, Chichester, The HVI guarantees strict monotonicity regarding Pareto dominance [ 17 , 18 ]. This makes the ranking of solution sets and hence algorithms possible for any given MO problem.
Nevertheless, other forms of metrics have also been developed and widely employed for benchmarking solution quality in MO optimization problems such as the convergence metric [ 19 ], diversity metric [ 20 ], and the HVI [ 21 ]. Over the past years, metaheuristic techniques have been applied with increasing frequency to industrial MO optimization problems. Some of the most effective metaheuristic techniques are the ones that spring from evolutionary and swarms approaches.
One such evolutionary approach is the genetic algorithm GA , introduced by Holland in the nineties [ 22 ]. GAs belongs to the group of stochastic search methods such as simulated annealing [ 23 ] and some forms of branch and bound. While most stochastic search techniques operate on a distinct solution for a particular problem, GAs operates on a population of solutions.
In recent times, GAs have been widely applied in industrial scenarios see [ 24 — 26 ]. Differential evolution DE is also a population-based evolutionary algorithm that has been derived from genetic algorithms GA [ 22 ]. DE was developed in the nineties by Storn and Price [ 27 ].
DE has been used extensively to solve problems which are nondifferentiable, noncontinuous, nonlinear, noisy, flat, multidimensional, and having many local minima, constraints, or high degree of stochasticity. Lately, DE has been applied to a variety of areas including optimization problems in chemical and process engineering [ 28 — 30 ].
One of the most popular swarm-based optimization approaches is the particle swarm optimization PSO algorithm. This optimization method was developed based on the movement and intelligence of swarms.
PSO was developed by Kennedy and Eberhart [ 31 ] in Lately, PSO has been applied to a variety of areas including optimization problems in engineering [ 32 ] as well as economic dispatch problems.
Another famed swarm approach is the gravitational search algorithm GSA. This method introduced recently by Rashedi et al. GSA belongs to the group of swarm-based stochastic search methods such as particle swarm optimization PSO [ 31 ] and cuckoo search algorithm CSA [ 34 ].
GSA operates on a population of solutions based on Newtonian law of gravity and mass interactions. This algorithm regards agents as objects consisting of different masses.
In recent times, GSA has been broadly applied in many industrial settings see [ 35 ]. In a nutshell, currently MO optimization is done with the aid of metaheuristic algorithms, scalarization techniques, and nondominated Pareto optimal solution tracing.
Besides, metric analysis is also employed to gauge the quality of the solutions obtained by any given algorithm. This paper aims to propose a philosophical framework that uses the information from metric analysis to generate high-performance and computationally efficient algorithms that produce high-quality solutions for MO optimization problems. This paper is organized as follows: Section 2 provides a brief review on some previous works on industrial applications of MO optimization techniques, while Section 3 presents an overview on some current issues arising in MO optimization and the proposed methodology for MO optimization.